{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Agentic RAG with semantic search and Claude\n",
    "\n",
    "This notebook demonstrates how to implement agentic retrieval-augmented generation (RAG) using multiple tools to retrieve data from both a web search and semantic search over a Pinecone index. We will cover the following steps:\n",
    "\n",
    "1. Setup: Setup and set Pinecone and Anthropic API keys\n",
    "2. Ingestion: Embedding and upserting data into Pinecone using integrated inference\n",
    "3. Tool setup: Setup of the web search tool and single-step tool use for retrieval\n",
    "4. Agentic workflow: Adding a second tool for semantic search over a Pinecone index for multi-tool, multi-turn chat workflows\n",
    "5. Clean up: Clean up and delete your index\n",
    "\n",
    "This notebook accompanies this [Retrieval-Augmented Generation article](https://www.pinecone.io/learn/retrieval-augmented-generation/)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Setup\n",
    "First, let's install the necessary libraries and set the API keys we will need to use in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -qU \\\n",
    "     anthropic==0.54.0 \\\n",
    "     pinecone==7.0.2 \\\n",
    "     pinecone-notebooks==0.1.1 \\\n",
    "     duckduckgo-search \\\n",
    "     datasets==3.6.0 \\\n",
    "     backoff==2.2.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get and set the Pinecone API key\n",
    "\n",
    "We will need a free [Pinecone API key](https://docs.pinecone.io/guides/get-started/quickstart). The code below will either authenticate you and set the API key as an environment variable or will prompt you to enter the API key and then set it in the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pinecone API key not found in environment.\n",
      "Pinecone API key saved to environment.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from getpass import getpass\n",
    "\n",
    "def get_pinecone_api_key():\n",
    "    \"\"\"\n",
    "    Get Pinecone API key from environment variable or prompt user for input.\n",
    "    Returns the API key as a string.\n",
    "\n",
    "    Only necessary for notebooks. When using Pinecone yourself, \n",
    "    you can use environment variables or the like to set your API key.\n",
    "    \"\"\"\n",
    "    api_key = os.environ.get(\"PINECONE_API_KEY\")\n",
    "    \n",
    "    if api_key is None:\n",
    "        try:\n",
    "            # Try Colab authentication if available\n",
    "            from pinecone_notebooks.colab import Authenticate\n",
    "            Authenticate()\n",
    "            # If successful, key will now be in environment\n",
    "            api_key = os.environ.get(\"PINECONE_API_KEY\")\n",
    "        except ImportError:\n",
    "            # If not in Colab or authentication fails, prompt user for API key\n",
    "            print(\"Pinecone API key not found in environment.\")\n",
    "            api_key = getpass(\"Please enter your Pinecone API key: \")\n",
    "            # Save to environment for future use in session\n",
    "            os.environ[\"PINECONE_API_KEY\"] = api_key\n",
    "            print(\"Pinecone API key saved to environment.\")\n",
    "            \n",
    "    return api_key\n",
    "\n",
    "PINECONE_API_KEY = get_pinecone_api_key()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set the Anthropic API key\n",
    "\n",
    "Next, we'll need to get a [Claude API key](https://docs.anthropic.com/en/api/overview). The code below will prompt you to enter it and then set it in the environment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anthropic API key not found in environment.\n",
      "Anthropic API key saved to environment.\n"
     ]
    }
   ],
   "source": [
    "def get_anthropic_api_key():\n",
    "    \"\"\"\n",
    "    Get Anthropic API key from environment variable or prompt user for input.\n",
    "    Returns the API key as a string.\n",
    "    \"\"\"\n",
    "\n",
    "    api_key = os.environ.get(\"ANTHROPIC_API_KEY\")\n",
    "    \n",
    "    if api_key is None:\n",
    "        print(\"Anthropic API key not found in environment.\")\n",
    "        try:\n",
    "            api_key = getpass(\"Please enter your Anthropic API key: \")\n",
    "            # Save to environment for future use in session\n",
    "            os.environ[\"ANTHROPIC_API_KEY\"] = api_key\n",
    "            print(\"Anthropic API key saved to environment.\")\n",
    "        except Exception as e:\n",
    "            print(f\"Error getting Anthropic API key: {e}\")\n",
    "            return None\n",
    "    \n",
    "    return api_key\n",
    "\n",
    "ANTHROPIC_API_KEY = get_anthropic_api_key()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Ingestion"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the dataset\n",
    "\n",
    "In this example, we'll show you how to build an agentic RAG workflow over a set of Arxiv paper abstracts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jenna/Development/playground/notebook-playground/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'id': '2401.04088',\n",
       " 'title': 'Mixtral of Experts',\n",
       " 'summary': 'We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model.\\nMixtral has the same architecture as Mistral 7B, with the difference that each\\nlayer is composed of 8 feedforward blocks (i.e. experts). For every token, at\\neach layer, a router network selects two experts to process the current state\\nand combine their outputs. Even though each token only sees two experts, the\\nselected experts can be different at each timestep. As a result, each token has\\naccess to 47B parameters, but only uses 13B active parameters during inference.\\nMixtral was trained with a context size of 32k tokens and it outperforms or\\nmatches Llama 2 70B and GPT-3.5 across all evaluated benchmarks. In particular,\\nMixtral vastly outperforms Llama 2 70B on mathematics, code generation, and\\nmultilingual benchmarks. We also provide a model fine-tuned to follow\\ninstructions, Mixtral 8x7B - Instruct, that surpasses GPT-3.5 Turbo,\\nClaude-2.1, Gemini Pro, and Llama 2 70B - chat model on human benchmarks. Both\\nthe base and instruct models are released under the Apache 2.0 license.',\n",
       " 'source': 'http://arxiv.org/pdf/2401.04088',\n",
       " 'authors': 'Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed',\n",
       " 'categories': 'cs.LG, cs.CL',\n",
       " 'comment': 'See more details at https://mistral.ai/news/mixtral-of-experts/',\n",
       " 'journal_ref': None,\n",
       " 'primary_category': 'cs.LG',\n",
       " 'published': '20240108',\n",
       " 'updated': '20240108',\n",
       " 'content': \"4 2 0 2\\nn a J 8 ] G L . s c [\\n1 v 8 8 0 4 0 . 1 0 4 2 : v i X r a\\n# Mixtral of Experts\\nAlbert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, LÃ©lio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, ThÃ©ophile Gervet, Thibaut Lavril, Thomas Wang, TimothÃ©e Lacroix, William El Sayed\\n\\nAbstract\\nWe introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model. Mixtral has the same architecture as Mistral 7B, with the difference that each layer is composed of 8 feedforward blocks (i.e. experts). For every token, at each layer, a router network selects two experts to process the current state and combine their outputs. Even though each token only sees two experts, the selected experts can be different at each timestep. As a result, each token has access to 47B parameters, but only uses 13B active parameters during inference. Mixtral was trained with a context size of 32k tokens and it outperforms or matches Llama 2 70B and GPT-3.5 across all evaluated benchmarks. In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks. We also provide a model fine- tuned to follow instructions, Mixtral 8x7B â\\x80\\x93 Instruct, that surpasses GPT-3.5 Turbo, Claude-2.1, Gemini Pro, and Llama 2 70B â\\x80\\x93 chat model on human bench- marks. Both the base and instruct models are released under the Apache 2.0 license.\\nCode: https://github.com/mistralai/mistral-src Webpage: https://mistral.ai/news/mixtral-of-experts/\\n# Introduction\\nIn this paper, we present Mixtral 8x7B, a sparse mixture of experts model (SMoE) with open weights, licensed under Apache 2.0. Mixtral outperforms Llama 2 70B and GPT-3.5 on most benchmarks. As it only uses a subset of its parameters for every token, Mixtral allows faster inference speed at low batch-sizes, and higher throughput at large batch-sizes.\\nMixtral is a sparse mixture-of-experts network. It is a decoder-only model where the feedforward block picks from a set of 8 distinct groups of parameters. At every layer, for every token, a router network chooses two of these groups (the â\\x80\\x9cexpertsâ\\x80\\x9d) to process the token and combine their output additively. This technique increases the number of parameters of a model while controlling cost and latency, as the model only uses a fraction of the total set of parameters per token.\\nMixtral is pretrained with multilingual data using a context size of 32k tokens. It either matches or exceeds the performance of Llama 2 70B and GPT-3.5, over several benchmarks. In particular,\\nMixture of Experts Layer i gating inputs af outputs router expert\\nFigure 1: Mixture of Experts Layer. Each input vector is assigned to 2 of the 8 experts by a router. The layerâ\\x80\\x99s output is the weighted sum of the outputs of the two selected experts. In Mixtral, an expert is a standard feedforward block as in a vanilla transformer architecture.\\nMixtral demonstrates superior capabilities in mathematics, code generation, and tasks that require multilingual understanding, significantly outperforming Llama 2 70B in these domains. Experiments show that Mixtral is able to successfully retrieve information from its context window of 32k tokens, regardless of the sequence length and the location of the information in the sequence.\\nWe also present Mixtral 8x7B â\\x80\\x93 Instruct, a chat model fine-tuned to follow instructions using supervised fine-tuning and Direct Preference Optimization [25]. Its performance notably surpasses that of GPT-3.5 Turbo, Claude-2.1, Gemini Pro, and Llama 2 70B â\\x80\\x93 chat model on human evaluation benchmarks. Mixtral â\\x80\\x93 Instruct also demonstrates reduced biases, and a more balanced sentiment profile in benchmarks such as BBQ, and BOLD. We release both Mixtral 8x7B and Mixtral 8x7B â\\x80\\x93 Instruct under the Apache 2.0 license1, free for academic and commercial usage, ensuring broad accessibility and potential for diverse applications. To enable the community to run Mixtral with a fully open-source stack, we submitted changes to the vLLM project, which integrates Megablocks CUDA kernels for efficient inference. Skypilot also allows the deployment of vLLM endpoints on any instance in the cloud.\\n# 2 Architectural details\\nMixtral is based on a transformer architecture [31] and uses the same modifications as described in [18], with the notable exceptions that Mix- tral supports a fully dense context length of 32k tokens, and the feed- forward blocks are replaced by Mixture-of-Expert layers (Section 2.1). The model architecture parameters are summarized in Table 1. Parameter Value\\ndim n_layers head_dim hidden_dim n_heads n_kv_heads context_len vocab_size num_experts top_k_experts\\n# 2.1 Sparse Mixture of Experts\\nWe present a brief overview of the Mixture of Experts layer (Figure 1). For a more in-depth overview, see [12]. The output of the MoE module for a given input x is determined by the weighted sum of the outputs of the expert networks, where the weights are given by the gating networkâ\\x80\\x99s output. i.e. given n expert networks {E0, Ei, ..., Enâ\\x88\\x921}, the output of the expert layer is given by: Table 1: Model architecture.\\n# j nâ\\x80\\x94\\nG(x)i Â· Ei(x). i=0\\nHere, G(x)i denotes the n-dimensional output of the gating network for the i-th expert, and Ei(x) is the output of the i-th expert network. If the gating vector is sparse, we can avoid computing the outputs of experts whose gates are zero. There are multiple alternative ways of implementing G(x) [6, 15, 35], but a simple and performant one is implemented by taking the softmax over the Top-K logits of a linear layer [28]. We use\\nG(x) := Softmax(TopK(x Â· Wg)),\\nwhere (TopK(â\\x84\\x93))i := â\\x84\\x93i if â\\x84\\x93i is among the top-K coordinates of logits â\\x84\\x93 â\\x88\\x88 Rn and (TopK(â\\x84\\x93))i := â\\x88\\x92â\\x88\\x9e otherwise. The value of K â\\x80\\x93 the number of experts used per token â\\x80\\x93 is a hyper-parameter that modu- lates the amount of compute used to process each token. If one increases n while keeping K fixed, one\\n# 1https://mistral.ai/news/mixtral-of-experts/\\n2\\n4096 32 128 14336 32 8 32768 32000 8 2\\ncan increase the modelâ\\x80\\x99s parameter count while keeping its computational cost effectively constant. This motivates a distinction between the modelâ\\x80\\x99s total parameter count (commonly referenced as the sparse parameter count), which grows with n, and the number of parameters used for processing an individual token (called the active parameter count), which grows with K up to n.\\nMoE layers can be run efficiently on single GPUs with high performance specialized kernels. For example, Megablocks [13] casts the feed-forward network (FFN) operations of the MoE layer as large sparse matrix multiplications, significantly enhancing the execution speed and naturally handling cases where different experts get a variable number of tokens assigned to them. Moreover, the MoE layer can be distributed to multiple GPUs through standard Model Parallelism techniques, and through a particular kind of partitioning strategy called Expert Parallelism (EP) [28]. During the MoE layerâ\\x80\\x99s execution, tokens meant to be processed by a specific expert are routed to the corresponding GPU for processing, and the expertâ\\x80\\x99s output is returned to the original token location. Note that EP introduces challenges in load balancing, as it is essential to distribute the workload evenly across the GPUs to prevent overloading individual GPUs or hitting computational bottlenecks.\\nIn a Transformer model, the MoE layer is applied independently per token and replaces the feed-forward (FFN) sub-block of the transformer block. For Mixtral we use the same SwiGLU architecture as the expert function Ei(x) and set K = 2. This means each token is routed to two SwiGLU sub-blocks with different sets of weights. Taking this all together, the output y for an input token x is computed as:\\nn-1 y= Ss Softmax(Top2(a - W,)); - SwiGLU;(a). i=0\\nThis formulation is similar to the GShard architecture [21], with the exceptions that we replace all FFN sub-blocks by MoE layers while GShard replaces every other block, and that GShard uses a more elaborate gating strategy for the second expert assigned to each token.\\n# 3 Results\\nWe compare Mixtral to Llama, and re-run all benchmarks with our own evaluation pipeline for fair comparison. We measure performance on a wide variety of tasks categorized as follow:\\nâ\\x80¢ Commonsense Reasoning (0-shot): Hellaswag [32], Winogrande [26], PIQA [3], SIQA [27], OpenbookQA [22], ARC-Easy, ARC-Challenge [8], CommonsenseQA [30]\\nWorld Knowledge (5-shot): NaturalQuestions [20], TriviaQA [19] â\\x80¢ Reading Comprehension (0-shot): BoolQ [7], QuAC [5] â\\x80¢ Math: GSM8K [9] (8-shot) with maj@8 and MATH [17] (4-shot) with maj@4 â\\x80¢ Code: Humaneval [4] (0-shot) and MBPP [1] (3-shot) â\\x80¢ Popular aggregated results: MMLU [16] (5-shot), BBH [29] (3-shot), and AGI Eval [34]\\n(3-5-shot, English multiple-choice questions only)\\n80 SE Mistral 78 = LLaMA27B = Sl LLaMA134B, jam Mistral 78 = LlaMA27B Ss LLAMA 1348, cee Mixtral 8x78 Sm LLaMA213BÂ° mmm LLaMA2 70B je Mixtral 8x78 mm LlaMA2138 lm LLaMA2 708 70 50 60 50 20 40 10 BH Code MMU Knowledge Reasoning â\\x80\\x98Comprehension AGI Eval Math â\\x80\\x98Accuracy (%)\\nFigure 2: Performance of Mixtral and different Llama models on a wide range of benchmarks. All models were re-evaluated on all metrics with our evaluation pipeline for accurate comparison. Mixtral outperforms or matches Llama 2 70B on all benchmarks. In particular, it is vastly superior in mathematics and code generation.\\n3\\nActive Params MMLU HellaS WinoG PIQA Arc-e Arc-c NQ TriQA HumanE MBPP Math GSM8K 7B 44.4% 77.1% 69.5% 77.9% 68.7% 43.2% 17.5% 56.6% 11.6% 26.1% 3.9% 16.0% 13B 55.6% 80.7% 72.9% 80.8% 75.2% 48.8% 16.7% 64.0% 18.9% 35.4% 6.0% 34.3% 33B 56.8% 83.7% 76.2% 82.2% 79.6% 54.4% 24.1% 68.5% 25.0% 40.9% 8.4% 44.1% 70B 69.9% 85.4% 80.4% 82.6% 79.9% 56.5% 25.4% 73.0% 29.3% 49.8% 13.8% 69.6% 7B 62.5% 81.0% 74.2% 82.2% 80.5% 54.9% 23.2% 62.5% 26.2% 50.2% 12.7% 50.0% 13B 70.6% 84.4% 77.2% 83.6% 83.1% 59.7% 30.6% 71.5% 40.2% 60.7% 28.4% 74.4%\\nTable 2: Comparison of Mixtral with Llama. Mixtral outperforms or matches Llama 2 70B performance on almost all popular benchmarks while using 5x fewer active parameters during inference.\\n70 Mixtral 8x7B. â\\x80\\x98Mixtral 8x7B Mixtral 8x7B 355 =o = Es & E60! Mistral 78 % 2681 Mistral 78 3 3 s0 5 = A % 66 50 g 4 45 64 78 138 348708 78 138 348708 78 138 348 70B S66 Mixtral 8x7B 50 Mixtral 8x7B 5 = 564 340 g al Mistral 78 ee Mistral 78 3 5 Â§ 30 5 eo â\\x80\\x94= Mistral Â° 20 â\\x80\\x94e LlaMA2 78 (138 348 70B 7B (138 348 708 7B Â«13B 34B 708 Active Params Active Params Active Params\\nFigure 3: Results on MMLU, commonsense reasoning, world knowledge and reading comprehension, math and code for Mistral (7B/8x7B) vs Llama 2 (7B/13B/70B). Mixtral largely outperforms Llama 2 70B on all benchmarks, except on reading comprehension benchmarks while using 5x lower active parameters. It is also vastly superior to Llama 2 70B on code and math.\\nDetailed results for Mixtral, Mistral 7B and Llama 2 7B/13B/70B and Llama 1 34B2 are reported in Table 2. Figure 2 compares the performance of Mixtral with the Llama models in different categories. Mixtral surpasses Llama 2 70B across most metrics. In particular, Mixtral displays a superior performance in code and mathematics benchmarks.\\nSize and Efficiency. We compare our performance to the Llama 2 family, aiming to understand Mixtral modelsâ\\x80\\x99 efficiency in the cost-performance spectrum (see Figure 3). As a sparse Mixture- of-Experts model, Mixtral only uses 13B active parameters for each token. With 5x lower active parameters, Mixtral is able to outperform Llama 2 70B across most categories.\\nNote that this analysis focuses on the active parameter count (see Section 2.1), which is directly proportional to the inference compute cost, but does not consider the memory costs and hardware utilization. The memory costs for serving Mixtral are proportional to its sparse parameter count, 47B, which is still smaller than Llama 2 70B. As for device utilization, we note that the SMoEs layer introduces additional overhead due to the routing mechanism and due to the increased memory loads when running more than one expert per device. They are more suitable for batched workloads where one can reach a good degree of arithmetic intensity.\\nComparison with Llama 2 70B and GPT-3.5. In Table 3, we report the performance of Mixtral 8x7B compared to Llama 2 70B and GPT-3.5. We observe that Mixtral performs similarly or above the two other models. On MMLU, Mixtral obtains a better performance, despite its significantly smaller capacity (47B tokens compared to 70B). For MT Bench, we report the performance of the latest GPT-3.5-Turbo model available, gpt-3.5-turbo-1106.\\n2Since Llama 2 34B was not open-sourced, we report results for Llama 1 34B.\\n4\\nLLaMA 2 70B GPT-3.5 MMLU (MCQ in 57 subjects) 69.9% 70.0% 70.6% HellaSwag (10-shot) 87.1% 85.5% 86.7% ARC Challenge (25-shot) 85.1% 85.2% 85.8% WinoGrande (5-shot) 83.2% 81.6% 81.2% MBPP (pass@1) 49.8% 52.2% 60.7% GSM-8K (5-shot) 53.6% 57.1% 58.4% MT Bench (for Instruct Models) 6.86 8.32 8.30\\n# Mixtral 8x7B\\nTable 3: Comparison of Mixtral with Llama 2 70B and GPT-3.5. Mixtral outperforms or matches Llama 2 70B and GPT-3.5 performance on most metrics.\\nEvaluation Differences. On some benchmarks, there are some differences between our evaluation protocol and the one reported in the Llama 2 paper: 1) on MBPP, we use the hand-verified subset 2) on TriviaQA, we do not provide Wikipedia contexts.\\n# 3.1 Multilingual benchmarks\\nCompared to Mistral 7B, we significantly upsample the proportion of multilingual data during pretraining. The extra capacity allows Mixtral to perform well on multilingual benchmarks while maintaining a high accuracy in English. In particular, Mixtral significantly outperforms Llama 2 70B in French, German, Spanish, and Italian, as shown in Table 4.\\nActive Params French Arc-c HellaS MMLU German Arc-c HellaS MMLU Spanish Arc-c HellaS MMLU Italian Arc-c HellaS MMLU 33B 70B 13B 42.9% 65.4% 49.0% 39.3% 68.1% 49.9% 49.9% 72.5% 64.3% 49.4% 70.9% 65.1% 58.2% 77.4% 70.9% 54.3% 73.0% 71.5% 55.4% 77.6% 72.5% 52.8% 75.1% 70.9% 41.1% 63.3% 48.7% 47.3% 68.7% 64.2% 45.7% 69.8% 52.3% 50.5% 74.5% 66.0%\\nTable 4: Comparison of Mixtral with Llama on Multilingual Benchmarks. On ARC Challenge, Hellaswag, and MMLU, Mixtral outperforms Llama 2 70B on 4 languages: French, German, Spanish, and Italian.\\n# 3.2 Long range performance\\nTo assess the capabilities of Mixtral to tackle long context, we evaluate it on the passkey retrieval task introduced in [23], a synthetic task designed to measure the ability of the model to retrieve a passkey inserted randomly in a long prompt. Results in Figure 4 (Left) show that Mixtral achieves a 100% retrieval accuracy regardless of the context length or the position of passkey in the sequence. Figure 4 (Right) shows that the perplexity of Mixtral on a subset of the proof-pile dataset [2] decreases monotonically as the size of the context increases.\\nPasskey Performance ry 0.8 0.6 04 0.2 0.0 OK 4K 8K 12K 16K 20K 24K 28K Seq Len Passkey Loc\\n3.8 â\\x80\\x94 Mixtral_8x7B 3.5 32 > $3.0 i] 228 fos a 2.0 0 5k 10k 15k 20k 25k 30k Context length\\nPasskey Performance ry 3.8 â\\x80\\x94 Mixtral_8x7B 3.5 0.8 32 > 0.6 $3.0 i] 228 04 fos 0.2 a 2.0 0.0 OK 4K 8K 12K 16K 20K 24K 28K 0 5k 10k 15k 20k 25k 30k Seq Len Context length\\nFigure 4: Long range performance of Mixtral. (Left) Mixtral has 100% retrieval accuracy of the Passkey task regardless of the location of the passkey and length of the input sequence. (Right) The perplexity of Mixtral on the proof-pile dataset decreases monotonically as the context length increases.\\n5\\n# 3.3 Bias Benchmarks\\nTo identify possible flaws to be corrected by fine-tuning / preference modeling, we measure the base model performance on Bias Benchmark for QA (BBQ) [24] and Bias in Open-Ended Language Generation Dataset (BOLD) [10]. BBQ is a dataset of hand-written question sets that target attested social biases against nine differ- ent socially-relevant categories: age, dis- ability status, gender identity, nationality, physical appearance, race/ethnicity, religion, socio-economic status, sexual orientation. BOLD is a large-scale dataset that consists of 23,679 English text generation prompts for bias benchmarking across five domains.\\nLlama 2 70B Mixtral 8x7B BBQ accuracy 51.5% 56.0% BOLD sentiment score (avg Â± std) gender profession religious_ideology political_ideology race 0.293 Â± 0.073 0.218 Â± 0.073 0.188 Â± 0.133 0.149 Â± 0.140 0.232 Â± 0.049 0.323 Â±0.045 0.243 Â± 0.087 0.144 Â± 0.089 0.186 Â± 0.146 0.232 Â± 0.052\\nFigure 5: Bias Benchmarks. Compared Llama 2 70B, Mixtral presents less bias (higher accuracy on BBQ, lower std on BOLD) and displays more positive sentiment (higher avg on BOLD).\\nWe benchmark Llama 2 and Mixtral on BBQ and BOLD with our evaluation framework and report the results in Table 5. Compared to Llama 2, Mixtral presents less bias on the BBQ benchmark (56.0% vs 51.5%). For each group in BOLD, a higher average sentiment score means more positive sentiments and a lower standard deviation indicates less bias within the group. Overall, Mixtral displays more positive sentiments than Llama 2, with similar variances within each group.\\n# Instruction Fine-tuning\\nWe train Mixtral â\\x80\\x93 Instruct using supervised fine-tuning (SFT) on an instruction dataset followed by Direct Preference Optimization (DPO) [25] on a paired feedback dataset. Mixtral â\\x80\\x93 Instruct reaches a score of 8.30 on MT-Bench [33] (see Table 2), making it the best open-weights model as of December 2023. Independent human evaluation conducted by LMSys is reported in Figure 63 and shows that Mixtral â\\x80\\x93 Instruct outperforms GPT-3.5-Turbo, Gemini Pro, Claude-2.1, and Llama 2 70B chat.\\nvs Arena Elo rating 1 MT-bench (score) License 1243 9.32 Proprietary 1192 8.96 Proprietary 1158 9.18 Proprietary Glaude-4 1149 7.9 Proprietary Claude-2.0 1131 8.06 Proprietary 1121 eS) Apache 2.0 Glaude-2.4 1117 8.18 Proprietary GPT-3..5-Turbo-9613 1117 8.39 Proprietary Gemini..Pro 1141 Proprietary Glas ta 1110 7.85 Proprietary Tulu-2-0P0-708 1110 7.89 AI2 ImpACT Low-risk Yi-34B-Chat 1110 Yi License GPT-3.5:Turbo-0314 1105 7.94 Proprietary Llama-2-79b-chat 1077 6.86 Llama 2 Community\\nFigure 6: LMSys Leaderboard. (Screenshot from Dec 22, 2023) Mixtral 8x7B Instruct v0.1 achieves an Arena Elo rating of 1121 outperforming Claude-2.1 (1117), all versions of GPT-3.5-Turbo (1117 best), Gemini Pro (1111), and Llama-2-70b-chat (1077). Mixtral is currently the best open-weights model by a large margin.\\n3https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard\\n6\\n# 5 Routing analysis\\nIn this section, we perform a small analysis on the expert selection by the router. In particular, we are interested to see if during training some experts specialized to some specific domains (e.g. mathematics, biology, philosophy, etc.).\\nTo investigate this, we measure the distribution of selected experts on different subsets of The Pile validation dataset [14]. Results are presented in Figure 7, for layers 0, 15, and 31 (layers 0 and 31 respectively being the first and the last layers of the model). Surprisingly, we do not observe obvious patterns in the assignment of experts based on the topic. For instance, at all layers, the distribution of expert assignment is very similar for ArXiv papers (written in Latex), for biology (PubMed Abstracts), and for Philosophy (PhilPapers) documents.\\nOnly for DM Mathematics we note a marginally different distribution of experts. This divergence is likely a consequence of the datasetâ\\x80\\x99s synthetic nature and its limited coverage of the natural language spectrum, and is particularly noticeable at the first and last layers, where the hidden states are very correlated to the input and output embeddings respectively.\\nThis suggests that the router does exhibit some structured syntactic behavior. Figure 8 shows examples of text from different domains (Python code, mathematics, and English), where each token is highlighted with a background color corresponding to its selected expert. The figure shows that words such as â\\x80\\x98selfâ\\x80\\x99 in Python and â\\x80\\x98Questionâ\\x80\\x99 in English often get routed through the same expert even though they involve multiple tokens. Similarly, in code, the indentation tokens are always assigned to the same experts, particularly at the first and last layers where the hidden states are more correlated to the input and output of the model.\\nWe also note from Figure 8 that consecutive tokens are often assigned the same experts. In fact, we observe some degree of positional locality in The Pile datasets. Table 5 shows the proportion of con- secutive tokens that get the same expert assignments per domain and layer. The proportion of repeated\\n0.20 0.15 0.10 0.05 layer: 15 0.20 0.15 0.10 0.05 layer: 31 Selection proportion 0.20 0.15 0.10 0.05 Expert ID | | ArXiv | Github | | PhilPapers | StackExchange | | DM Mathematics | | Gutenberg | | PubMed Abstracts | | Wikipedia (en)\\nFigure 7: Proportion of tokens assigned to each expert on different domains from The Pile dataset for layers 0, 15, and 31. The gray dashed vertical line marks 1/8, i.e. the proportion expected with uniform sampling. Here, we consider experts that are either selected as a first or second choice by the router. A breakdown of the proportion of assignments done in each case cane be seen in Figure 9 in the Appendix.\\n7\\nLayer 0 First choice Layer 15 Layer 31 Layer 0 First or second choice Layer 15 Layer 31 ArXiv DM Mathematics Github Gutenberg PhilPapers PubMed Abstracts StackExchange Wikipedia (en) 14.0% 14.1% 14.9% 13.9% 13.6% 14.2% 13.6% 14.4% 27.9% 28.4% 28.1% 26.1% 25.3% 24.6% 27.2% 23.6% 22.7% 19.7% 19.7% 26.3% 22.1% 22.0% 23.6% 25.3% 46.5% 44.9% 49.9% 49.5% 46.9% 48.6% 48.2% 49.8% 62.3% 67.0% 66.9% 63.1% 61.9% 61.6% 64.6% 62.1% 52.9% 44.5% 49.2% 52.2% 51.3% 51.8% 53.6% 51.8%\\nTable 5: Percentage of expert assignment repetitions. We evaluate the proportion of times the same expert is assigned to a token i and its following token i+1. We report whether the first chosen expert is the same, or whether the same expert is observed as first or second choice in consecutive tokens. For reference, the expected proportion of repetitions in the case of random assignments is 1 5 7 â\\x89\\x88 46% for â\\x80\\x9cFirst and second choiceâ\\x80\\x9d. Repetitions at the first layer are close to random, but are significantly higher at layers 15 and 31. The high number of repetitions shows that expert choice exhibits high temporal locality at these layers.\\nconsecutive assignments is significantly higher than random for higher layers. This has implications in how one might optimize the model for fast training and inference. For example, cases with high locality are more likely to cause over-subscription of certain experts when doing Expert Parallelism. Conversely, this locality can be leveraged for caching, as is done in [11]. A more complete view of these same expert frequency is provided for all layers and across datasets in Figure 10 in the Appendix.\\n# 6 Conclusion\\nIn this paper, we introduced Mixtral 8x7B, the first mixture-of-experts network to reach a state-of-the- art performance among open-source models. Mixtral 8x7B Instruct outperforms Claude-2.1, Gem- ini Pro, and GPT-3.5 Turbo on human evaluation benchmarks. Because it only uses two experts at each time step, Mixtral only uses 13B active parameters per token while outperforming the previous best model using 70B parameters per token (Llama 2 70B). We are making our trained and fine-tuned mod- els publicly available under the Apache 2.0 license. By sharing our models, we aim to facilitate the de- velopment of new techniques and applications that can benefit a wide range of industries and domains.\\nLayer 0 Layer 15 Layer 31 class MoeLayer(nn. Module) : â\\x80\\x9cinit__(self, experts//List [nn.Modutel,) | Super (V7 init assert len(experts) > 0 self. experts = nn.ModuleList((experts) self. gate = gate self.args = moe_args def forward(self, inputs: torch.Tensor): inputs _squashed = inputs. view(-1,_ inputs.| gate_logits = self.gatel inputs_squashed) weights, selected_experts = torch. topk( gate_logits, Self-args.nun_experts_Ã© weights! = nri.|funct ional softinax'( weights, din=1, dtype=torch. float, ).type_as|(inputs) results| = torch. zeros_ ike! linputs_squashe for i, expert in enunerate(self. experts): batch_idx,! nth_expert = torch. wnere( results [batch_idx] += weights [batch_i input s_squashed [batch_idx] ) return resutts:.view las{(inputs) class NoeLayer (nn. Module) = def _ init__(self, experts! List'{nri.Modulelly Super (Tz init_t assert len (experts) > 9) self.experts = nn. ModuleList((experits)) def forward(self, inputs: torch. Tensor)?! inputs_squashed = inputs.View(-1) inputs) gate_logits = self.gatel inputs_squashed) weights, selected_experts = torch. topk( getellogits, self.argssnun_experts pe weightsâ\\x80\\x99 = nn. functionallsoftmax(Â® Weights, dtypextorch. floaty ) type_as (inputs) results| = torch. zerdsillikel(input siiequashe| for i, expert in enumerate (self. experts): batch idx, nth_expert = torch.where(s results [batch_idx] += weights [batch_iÂ¢ inputs|_squashed[batch idx], y return resultsiiview jas (inputs) class| MoeLayer(nn. Module): def init__(self, expertsâ\\x80\\x99 List|fifi.Modulel) Super(Ve_init_O) assert len(experts) > 0 self, experts = nn.ModuleListl(@xperits)) self. gate = gate Self.args = moe_args def forward(self, inputs: torch. Tensor): inputs_squashed = inputs.view(=1, inputs) gate_logits = self.gate( inputs_squashed) weights, selected_experts = torch. topk( gate_logits, self.argssfum_experts_pe weights) nni.unct iorial.isoftinax( YP Yiitype_as (inputs) results = torch. zerosillikel(inputslisquashe| for i, expert in enunerate(self.experts): batch_idx, nth_expert = torch.where(s results [batch_idx] += weights [batch_iÂ¢ inputs_squashed [batch_idx] ) return) results\\\\iviewilas|(inputs)) Tuestiond] Solve â\\x80\\x94AINr 27K SLIT! and SORT, lanswers 4 Question?â\\x80\\x99 Calculate Baiasoazusaaly 4111270 iAnswer: -841469015.544 (Question! Letâ\\x80\\x99 x(gy = 94g # Hl Let! q(clJ= Zee #] IAnswer: S4ea - 30 â\\x80\\x98Question#! Solve Azer Â¥ 27HE = Ate and 1505 lanswer:) 4 Calculate ~eaieseiaz. saa Â¥ 417127. ~841469015.544 â\\x80\\x98Answer: (Questor â\\x80\\x98Answer: etâ\\x80\\x99 x(q) = 9*g Â¥ Wl Let! ql)! = 2eele Sara â\\x80\\x94 30 question Solve -42Â¥e1E B7eC= â\\x80\\x94Ad67 and 130%] answers \\\\questionÂ®| calculate savesona2.saq + auaz7. Answer: -847469015.544 â\\x80\\x98OÂ¥o)H A Let q(el = (questiond! Let! x(a) = awed | Answers 54a ~ â\\x80\\x98A model airplane flies stower when flying into tt jwind and faster with wind at its back. when Launcl Iright angles to the wind,â\\x80\\x9d cross wind,| its groun Icompared with! flying in still air is (A) the same (B) greater (C) less (0)! either! grea lor less dependingâ\\x80\\x99 on wind speed i nodelaitp ane) URE slover when flying into eH lind and faster with wind at its back. When) launch Tight angles to the wind, a cross wind,. its) grounc Compared with â\\x80\\x98lying in stitt air is (A) the same (18) greater) (C) less (D)! either grea lor less depending on wind speed H model airplane flies slower! when flying inte th wind and faster with wind at its backâ\\x80\\x99. When Launcl [right angles to the wind, a cross wind, its grounc Icompared with flying in still air is (A) the sane (B) greater (C) less (0)! either gree jor less depending on wind speed\\nFigure 8: Text samples where each token is colored with the first expert choice. The selection of experts appears to be more aligned with the syntax rather than the domain, especially at the initial and final layers.\\n8\\n# Acknowledgements\\nWe thank the CoreWeave and Scaleway teams for technical support as we trained our models. We are grateful to NVIDIA for supporting us in integrating TensorRT-LLM and Triton and working alongside us to make a sparse mixture of experts compatible with TensorRT-LLM.\\n# References\\n[1] Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al. Program synthesis with large language models. arXiv preprint arXiv:2108.07732, 2021.\\n[2] Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, Marco Dos Santos, Stephen McAleer, Albert Q Jiang, Jia Deng, Stella Biderman, and Sean Welleck. Llemma: An open language model for mathematics. arXiv preprint arXiv:2310.10631, 2023.\\n[3] Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al. Piqa: Reasoning about phys- ical commonsense in natural language. In Proceedings of the AAAI conference on artificial intelligence, pages 7432â\\x80\\x937439, 2020.\\n[4] Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374, 2021.\\n[5] Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. Quac: Question answering in context. arXiv preprint arXiv:1808.07036, 2018.\\n[6] Aidan Clark, Diego De Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake Hechtman, Trevor Cai, Sebastian Borgeaud, et al. Unified scaling laws for routed language models. In International Conference on Machine Learning, pages 4057â\\x80\\x934086. PMLR, 2022.\\n[7] Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. Boolq: Exploring the surprising difficulty of natural yes/no questions. arXiv preprint arXiv:1905.10044, 2019.\\n[8] Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. Think you have solved question answering? try arc, the ai2 reasoning challenge. arXiv preprint arXiv:1803.05457, 2018.\\n[9] Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al. Training verifiers to solve math word problems. arXiv preprint arXiv:2110.14168, 2021.\\n[10] Jwala Dhamala, Tony Sun, Varun Kumar, Satyapriya Krishna, Yada Pruksachatkun, Kai-Wei Chang, and Rahul Gupta. Bold: Dataset and metrics for measuring biases in open-ended language generation. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pages 862â\\x80\\x93872, 2021.\\n[11] Artyom Eliseev and Denis Mazur. Fast inference of mixture-of-experts language models with offloading. arXiv preprint arXiv:2312.17238, 2023.\\n[12] William Fedus, Jeff Dean, and Barret Zoph. A review of sparse expert models in deep learning. arXiv preprint arXiv:2209.01667, 2022.\\n[13] Trevor Gale, Deepak Narayanan, Cliff Young, and Matei Zaharia. Megablocks: Efficient sparse training with mixture-of-experts. arXiv preprint arXiv:2211.15841, 2022.\\n[14] Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. The pile: An 800gb dataset of diverse text for language modeling. arXiv preprint arXiv:2101.00027, 2020.\\n[15] Hussein Hazimeh, Zhe Zhao, Aakanksha Chowdhery, Maheswaran Sathiamoorthy, Yihua Chen, Rahul Mazumder, Lichan Hong, and Ed Chi. Dselect-k: Differentiable selection in the mixture of experts with applications to multi-task learning. Advances in Neural Information Processing Systems, 34:29335â\\x80\\x9329347, 2021.\\n9\\n[16] Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. Measuring massive multitask language understanding. arXiv preprint arXiv:2009.03300, 2020.\\n[17] Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. arXiv preprint arXiv:2103.03874, 2021.\\n[18] Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al. Mistral 7b. arXiv preprint arXiv:2310.06825, 2023.\\n[19] Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551, 2017.\\n[20] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. Natural questions: a benchmark for question answering research. Transactions of the Association for Computational Linguistics, pages 453â\\x80\\x93466, 2019.\\n[21] Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, and Zhifeng Chen. Gshard: Scaling giant models with condi- tional computation and automatic sharding. arXiv preprint arXiv:2006.16668, 2020.\\n[22] Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. Can a suit of armor conduct electricity? a new dataset for open book question answering. arXiv preprint arXiv:1809.02789, 2018.\\n[23] Amirkeivan Mohtashami and Martin Jaggi. Landmark attention: Random-access infinite context length for transformers. arXiv preprint arXiv:2305.16300, 2023.\\n[24] Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thomp- son, Phu Mon Htut, and Samuel R Bowman. Bbq: A hand-built bias benchmark for question answering. arXiv preprint arXiv:2110.08193, 2021.\\n[25] Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. arXiv preprint arXiv:2305.18290, 2023.\\n[26] Keisuke Sakaguchi, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. Winogrande: An adversarial winograd schema challenge at scale. Communications of the ACM, pages 99â\\x80\\x93106, 2021.\\n[27] Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, and Yejin Choi. Socialiqa: Com- monsense reasoning about social interactions. arXiv preprint arXiv:1904.09728, 2019.\\n[28] Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538, 2017.\\n[29] Mirac Suzgun, Nathan Scales, Nathanael SchÃ¤rli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, , and Jason Wei. Challenging big-bench tasks and whether chain-of-thought can solve them. arXiv preprint arXiv:2210.09261, 2022.\\n[30] Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. Commonsenseqa: A ques- tion answering challenge targeting commonsense knowledge. arXiv preprint arXiv:1811.00937, 2018.\\n[31] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Å\\x81ukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.\\n[32] Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830, 2019.\\n[33] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, et al. Judging llm-as-a-judge with mt-bench and chatbot arena. arXiv preprint arXiv:2306.05685, 2023.\\n10\\n[34] Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, and Nan Duan. Agieval: A human-centric benchmark for evaluating foundation models. arXiv preprint arXiv:2304.06364, 2023.\\n[35] Yanqi Zhou, Tao Lei, Hanxiao Liu, Nan Du, Yanping Huang, Vincent Zhao, Andrew M Dai, Quoc V Le, James Laudon, et al. Mixture-of-experts with expert choice routing. Advances in Neural Information Processing Systems, 35:7103â\\x80\\x937114, 2022.\\n11\\n# Either choice\\n0\\nLayer -- 0.3 0.2 0 Layer 0 -- First choice 0.3 Layer 0 -- Second choice 0.3 < 2 t Layer 15 -- First choice fe} Q 0.3 Â° a 0.2 el (el er rere! ie it len | ie} o 0 v Layer 15 -- Second choice 8 03 0.2 0 Layer 31 -- Either choice\\n# Expert ID\\nArXiv Github PhilPapers. StackExchange |_| | |_| | | DM Mathematics | Gutenberg || PubMed Abstracts | Wikipedia (en)\\nFigure 9: Proportion of tokens assigned to each expert on different subsets from The Pile dataset, separated by whether the expert was selected as first or second choice, or either. The â\\x80\\x9cEither choiceâ\\x80\\x9d case is equivalent to Figure 7. The gray dashed vertical line marks 1\\n12\\nFirst choice 9 w is) Â° N a Â° N is) Â° An wu 0.7 0.6 Proportion of repeated assignments 0.5 Layer source â\\x80\\x94e ArXiv â\\x80\\x94eâ\\x80\\x94 DM Mathematics â\\x80\\x94e Github â\\x80\\x94eâ\\x80\\x94 Gutenberg â\\x80\\x94eâ\\x80\\x94 PhilPapers â\\x80\\x94eâ\\x80\\x94 PubMed â\\x80\\x94e- StackExchange â\\x80\\x94e-â\\x80\\x94 Wikipedia (en)\\n# Abstracts\\nFigure 10: Repeated consecutive assignments per MoE layer. Repeated assignments occur a lot more often than they would with uniform assignments (materialized by the dashed lines). Patterns are similar across datasets with less repetitions for DM Mathematics.\\n13\",\n",
       " 'references': {'id': '1905.07830'}}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"jamescalam/ai-arxiv2\", split=\"train\")\n",
    "\n",
    "#Let's take a peek at the data\n",
    "dataset[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess the dataset\n",
    "\n",
    "We'll need to reorient our dataset to the records format, to support using Pinecone Integrated Inference. We only need some of the columns and we want the summary column renamed to chunk_text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'id': '2401.04088',\n",
       " 'title': 'Mixtral of Experts',\n",
       " 'chunk_text': 'We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model.\\nMixtral has the same architecture as Mistral 7B, with the difference that each\\nlayer is composed of 8 feedforward blocks (i.e. experts). For every token, at\\neach layer, a router network selects two experts to process the current state\\nand combine their outputs. Even though each token only sees two experts, the\\nselected experts can be different at each timestep. As a result, each token has\\naccess to 47B parameters, but only uses 13B active parameters during inference.\\nMixtral was trained with a context size of 32k tokens and it outperforms or\\nmatches Llama 2 70B and GPT-3.5 across all evaluated benchmarks. In particular,\\nMixtral vastly outperforms Llama 2 70B on mathematics, code generation, and\\nmultilingual benchmarks. We also provide a model fine-tuned to follow\\ninstructions, Mixtral 8x7B - Instruct, that surpasses GPT-3.5 Turbo,\\nClaude-2.1, Gemini Pro, and Llama 2 70B - chat model on human benchmarks. Both\\nthe base and instruct models are released under the Apache 2.0 license.',\n",
       " 'source': 'http://arxiv.org/pdf/2401.04088',\n",
       " 'authors': 'Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed',\n",
       " 'categories': 'cs.LG, cs.CL',\n",
       " 'primary_category': 'cs.LG',\n",
       " 'published': '20240108',\n",
       " 'updated': '20240108'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset.remove_columns([\"content\", \"comment\", \"journal_ref\", \"references\"])\n",
    "dataset = dataset.rename_column(\"summary\", \"chunk_text\")\n",
    "\n",
    "dataset=dataset.to_list()\n",
    "dataset[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pinecone vector database and integrated inference\n",
    "\n",
    "We'll implement one of our retrieval tools using semantic search, but first we need to create a Pinecone index and upsert vectors into it.\n",
    "\n",
    "We'll use integrated inference, so when creating the indexes, we'll specify a Pinecone-hosted model to use for embedding queries and documents. Pinecone handles the embedding for us, so we can pass it text directly. When we search, we'll use integrated reranking to return the most relevant matches.\n",
    "\n",
    "Learn more about:\n",
    "- Semantic search [here](https://docs.pinecone.io/guides/search/semantic-search)\n",
    "- Integrated inference [here](https://docs.pinecone.io/guides/index-data/indexing-overview#integrated-embedding)\n",
    "- Integrated reranking [here](https://docs.pinecone.io/guides/search/rerank-results#integrated-reranking)\n",
    "\n",
    "Now, we can initialize the dense index, using the `llama-text-embed-v2` embedding model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dimension': 1024,\n",
       " 'index_fullness': 0.0,\n",
       " 'metric': 'cosine',\n",
       " 'namespaces': {},\n",
       " 'total_vector_count': 0,\n",
       " 'vector_type': 'dense'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pinecone import Pinecone\n",
    "\n",
    "pc = Pinecone(\n",
    "    api_key=PINECONE_API_KEY,\n",
    "    # You can remove this parameter for your own projects\n",
    "    source_tag=\"pinecone_examples:learn:generation:agentic_rag:agentic_rag_with_claude\"\n",
    ")\n",
    "\n",
    "index_name = \"agentic-rag-with-claude\"\n",
    "if not pc.has_index(index_name):\n",
    "    pc.create_index_for_model(\n",
    "        name=index_name,\n",
    "        cloud=\"aws\",\n",
    "        region=\"us-east-1\",\n",
    "        # Chunk text will be the field we embed from our documents\n",
    "        embed={\n",
    "            \"model\":\"llama-text-embed-v2\",\n",
    "            \"field_map\":{\"text\": \"chunk_text\"}\n",
    "        }\n",
    "    )\n",
    "\n",
    "index = pc.Index(index_name)\n",
    "index.describe_index_stats()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should see that the new Pinecone index has a total_vector_count of 0, as we haven't added any vectors yet.\n",
    "\n",
    "Now, whenever we upsert to or query an index, Pinecone will automatically embed the text data with the specified embedding model.\n",
    "\n",
    "To learn generally how dense models are trained and why, check out our guide [here](https://www.pinecone.io/learn/the-practitioners-guide-to-e5/)."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Embedding and upserting data to Pinecone \n",
    "\n",
    "With our index set up, we can now take our data and upsert them to the index. Here is a useful upsertion script that handles rate limits nicely. We [upsert in batches of 96](https://docs.pinecone.io/guides/index-data/upsert-data#upsert-in-batches), because this is the batch size limit for hosted models on Pinecone. This should take about a minute to upsert all of our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Upserting records batch: 100%|██████████| 28/28 [00:26<00:00,  1.06it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "import backoff\n",
    "\n",
    "batch_size = 96\n",
    "\n",
    "@backoff.on_exception(backoff.expo, Exception, max_tries=8, max_time=80, on_backoff=lambda details: print(f\"Backoff: {details['tries']} of 8\"))\n",
    "def upsert_in_batches(dataset, index, batch_size):\n",
    "    for start in tqdm(range(0, len(dataset), batch_size), desc=\"Upserting records batch\"):\n",
    "        batch = dataset[start:start + batch_size]\n",
    "        index.upsert_records(namespace=\"arxiv\", records=batch)\n",
    "\n",
    "upsert_in_batches(dataset, index, batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "index stats:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'dimension': 1024,\n",
       " 'index_fullness': 0.0,\n",
       " 'metric': 'cosine',\n",
       " 'namespaces': {'arxiv': {'vector_count': 2673}},\n",
       " 'total_vector_count': 2673,\n",
       " 'vector_type': 'dense'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"index stats:\")\n",
    "index.describe_index_stats()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Tool setup\n",
    "\n",
    "### Tool concepts\n",
    "\n",
    "#### Tools\n",
    "\n",
    "RAG gives LLMs the capability to ground their responses in data retrieved from an external data source like a Pinecone vector database.\n",
    "\n",
    "![Simple RAG]()\n",
    "\n",
    "Tool use extends/generalizes this concept. A tool can be considered any service that an LLM can communicate with (eg: a search engine, a SQL database, a service like Notion, a python interpreter, etc.). An LLM can be connected to one or more tools.\n",
    "\n",
    "![How LLMs can be extended by calling tools]()\n",
    "\n",
    "#### Decision making\n",
    "\n",
    "With tool use, the LLM acts as decision-making engine. It has to decide:\n",
    "1. Whether a user query can be answered directly or requires some external context from a tool\n",
    "2. If tools are required, which tools would those be, and how to call them.\n",
    "\n",
    "![How the LLM acts as a decision-making engine]()\n",
    "\n",
    "#### Actions\n",
    "\n",
    "While RAG is a read-only setup, tool use enables developers to build applications that can take actions (i.e read and write to external systems via APIs).\n",
    "\n",
    "![Agentic system supporting tools that can take actions]()\n",
    "\n",
    "#### Function calling\n",
    "\n",
    "If tool use is the \"what\" of LLM capability extension, function calling is the \"how\". Function calling is a specific implementation of tool use, focusing on the technical details of picking the tools to call and generating the payload to call the tool.\n",
    "\n",
    "### Setup \n",
    "\n",
    "We'll setup two tools for retrieval: a web search tool and a semantic search tool.\n",
    "\n",
    "While Anthropic does have a built in web search tool, we'll use the Duckduckgo search engine for demonstration purposes. A tool can be any function or service that can receive and send data - an email service, an SQL database, a vector database, a weather data service, or even another LLM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from duckduckgo_search import DDGS\n",
    "\n",
    "# Tool 1: Web search engine\n",
    "def web_search_engine(query: str) -> dict:\n",
    "    \n",
    "    search_results = DDGS().text(query, max_results=5)\n",
    "\n",
    "    return search_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'title': 'LOUBNA BEN ALLAL - Google Scholar',\n",
       "  'href': 'https://scholar.google.com/citations?user=reU1i-sAAAAJ',\n",
       "  'body': '\\u202aHugging Face\\u202c - \\u202a\\u202aCited by 4,518\\u202c\\u202c - \\u202aMachine Learning\\u202c'},\n",
       " {'title': 'Loubna Ben allal | Papers With Code',\n",
       "  'href': 'https://paperswithcode.com/author/loubna-ben-allal',\n",
       "  'body': 'The BigCode Project Governance Card no code implementations • 6 Dec 2023 • BigCode collaboration, Sean Hughes, Harm de Vries, Jennifer Robinson, Carlos Muñoz Ferrandis, Loubna Ben allal, Leandro von Werra, Jennifer Ding, Sebastien Paquet, Yacine Jernite This document serves as an overview of the different mechanisms and areas of governance in the BigCode project.'},\n",
       " {'title': '[2406.17557] The FineWeb Datasets: Decanting the Web for the Finest ...',\n",
       "  'href': 'https://arxiv.org/abs/2406.17557',\n",
       "  'body': 'The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, Thomas Wolf'},\n",
       " {'title': 'Loubna Ben Allal - Home - ACM Digital Library',\n",
       "  'href': 'https://dl.acm.org/profile/99661625996',\n",
       "  'body': \"Colleague Collaboration Author's Latest Publications research-article Scaling laws and compute-optimal training beyond fixed training durations Alexander Hägele, Elie Bakouch, Atli Kosson, + 3\"},\n",
       " {'title': 'loubnabnl (Loubna Ben Allal) - Hugging Face',\n",
       "  'href': 'https://huggingface.co/loubnabnl',\n",
       "  'body': 'Papers11 arxiv:2506.05209 arxiv:2504.05299 arxiv:2502.02737 arxiv:2406.17557 Expand 11 papers spaces6'}]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "web_search_engine(\"Papers by researcher Loubna Ben Allal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# map tool names to functions\n",
    "functions_map = {\n",
    "    \"web_search_engine\": web_search_engine,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the Tool Schema\n",
    "\n",
    "The schema is what will be passed to the Anthropic API when running a tool use workflow. The schema informs the LLM about what the tool does, and the LLM decides whether to use a particular tool based on it. Therefore, the more descriptive and specific the schema, the more likely the LLM will make the right tool call decisions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "web_search_engine_tool = {\n",
    "    \"name\": \"web_search_engine\",\n",
    "    \"description\": \"Searches the internet and retrieves content relevant to the input query\",\n",
    "    \"input_schema\": {\n",
    "        \"type\": \"object\",\n",
    "        \"properties\": {\n",
    "            \"query\": {\n",
    "                \"type\": \"string\",\n",
    "                \"description\": \"The search query.\"\n",
    "            }\n",
    "        },\n",
    "        \"required\": [\"query\"]\n",
    "    }\n",
    "}\n",
    "\n",
    "tools = [\n",
    "  web_search_engine_tool\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Review tool_use response\n",
    "\n",
    "Let's check out the response from Claude when it decides a tool should be used. Note the tool use block that indicates which tool function we should call and what the function parameters are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"What papers has Loubna Ben Allal written?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Message(id='msg_01D87dkaiFSbkVPpYWbDJ2tH', content=[ToolUseBlock(id='toolu_014MUwZz4wFRRkU1zq9hXKPJ', input={'query': 'Loubna Ben Allal research papers published'}, name='web_search_engine', type='tool_use')], model='claude-3-5-haiku-20241022', role='assistant', stop_reason='tool_use', stop_sequence=None, type='message', usage=Usage(cache_creation_input_tokens=0, cache_read_input_tokens=0, input_tokens=355, output_tokens=65, server_tool_use=None, service_tier='standard'))\n"
     ]
    }
   ],
   "source": [
    "import anthropic\n",
    "\n",
    "client = anthropic.Anthropic()\n",
    "model = \"claude-3-5-haiku-latest\"\n",
    "messages = []\n",
    "\n",
    "messages.append({\n",
    "    \"role\": \"user\",\n",
    "    \"content\": [\n",
    "        {\n",
    "            \"type\": \"text\",\n",
    "            \"text\": f\"{query}\"\n",
    "        }\n",
    "    ]\n",
    "})\n",
    "\n",
    "response = client.messages.create(\n",
    "    model=model,\n",
    "    max_tokens=1000,\n",
    "    temperature=1,\n",
    "    tools=tools,\n",
    "    messages=messages\n",
    ")\n",
    "\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Invoke the tool and send tool_result back to Claude\n",
    "\n",
    "Here we invoke the tool, append the tool_result user block and send it back to Claude. The LLM will reason over the tool results to generate a response for the user."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TOOL_RESULT:\n",
      "[{'title': 'LOUBNA BEN ALLAL - Google Scholar', 'href': 'https://scholar.google.com/citations?user=reU1i-sAAAAJ', 'body': '\\u202aHugging Face\\u202c - \\u202a\\u202aCited by 4,518\\u202c\\u202c - \\u202aMachine Learning\\u202c'}, {'title': 'Loubna Ben allal | Papers With Code', 'href': 'https://paperswithcode.com/author/loubna-ben-allal', 'body': 'The BigCode Project Governance Card no code implementations • 6 Dec 2023 • BigCode collaboration, Sean Hughes, Harm de Vries, Jennifer Robinson, Carlos Muñoz Ferrandis, Loubna Ben allal, Leandro von Werra, Jennifer Ding, Sebastien Paquet, Yacine Jernite This document serves as an overview of the different mechanisms and areas of governance in the BigCode project.'}, {'title': 'Loubna Ben Allal - Home - ACM Digital Library', 'href': 'https://dl.acm.org/profile/99661625996', 'body': \"Colleague Collaboration Author's Latest Publications research-article Scaling laws and compute-optimal training beyond fixed training durations Alexander Hägele, Elie Bakouch, Atli Kosson, + 3\"}, {'title': '[2406.17557] The FineWeb Datasets: Decanting the Web for the Finest ...', 'href': 'https://arxiv.org/abs/2406.17557', 'body': 'The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, Thomas Wolf'}, {'title': 'loubnabnl (Loubna Ben Allal) - Hugging Face', 'href': 'https://huggingface.co/loubnabnl', 'body': 'Papers11 arxiv:2506.05209 arxiv:2504.05299 arxiv:2502.02737 arxiv:2406.17557 Expand 11 papers spaces6'}]\n",
      "SENT MESSAGES\n",
      "[{'role': 'user', 'content': [{'type': 'text', 'text': 'What papers has Loubna Ben Allal written?'}]}, {'role': 'assistant', 'content': [ToolUseBlock(id='toolu_014MUwZz4wFRRkU1zq9hXKPJ', input={'query': 'Loubna Ben Allal research papers published'}, name='web_search_engine', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_014MUwZz4wFRRkU1zq9hXKPJ', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'LOUBNA BEN ALLAL - Google Scholar\\', \\'href\\': \\'https://scholar.google.com/citations?user=reU1i-sAAAAJ\\', \\'body\\': \\'\\\\u202aHugging Face\\\\u202c - \\\\u202a\\\\u202aCited by 4,518\\\\u202c\\\\u202c - \\\\u202aMachine Learning\\\\u202c\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'Loubna Ben allal | Papers With Code\\', \\'href\\': \\'https://paperswithcode.com/author/loubna-ben-allal\\', \\'body\\': \\'The BigCode Project Governance Card no code implementations • 6 Dec 2023 • BigCode collaboration, Sean Hughes, Harm de Vries, Jennifer Robinson, Carlos Muñoz Ferrandis, Loubna Ben allal, Leandro von Werra, Jennifer Ding, Sebastien Paquet, Yacine Jernite This document serves as an overview of the different mechanisms and areas of governance in the BigCode project.\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'Loubna Ben Allal - Home - ACM Digital Library\\', \\'href\\': \\'https://dl.acm.org/profile/99661625996\\', \\'body\\': \"Colleague Collaboration Author\\'s Latest Publications research-article Scaling laws and compute-optimal training beyond fixed training durations Alexander Hägele, Elie Bakouch, Atli Kosson, + 3\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'[2406.17557] The FineWeb Datasets: Decanting the Web for the Finest ...\\', \\'href\\': \\'https://arxiv.org/abs/2406.17557\\', \\'body\\': \\'The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, Anton Lozhkov, Margaret Mitchell, Colin Raffel, Leandro Von Werra, Thomas Wolf\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'loubnabnl (Loubna Ben Allal) - Hugging Face\\', \\'href\\': \\'https://huggingface.co/loubnabnl\\', \\'body\\': \\'Papers11 arxiv:2506.05209 arxiv:2504.05299 arxiv:2502.02737 arxiv:2406.17557 Expand 11 papers spaces6\\'}}]'}]}]\n",
      "RESPONSE:\n",
      "Message(id='msg_01WzULDXQvXXZFmJUoZAUgXV', content=[TextBlock(citations=None, text='Let me do another search to get more details about her papers:', type='text'), ToolUseBlock(id='toolu_01WHVoGQsdoFvyZq9tFfoJm8', input={'query': 'Loubna Ben Allal notable research papers 2023-2024'}, name='web_search_engine', type='tool_use')], model='claude-3-5-haiku-20241022', role='assistant', stop_reason='tool_use', stop_sequence=None, type='message', usage=Usage(cache_creation_input_tokens=0, cache_read_input_tokens=0, input_tokens=1067, output_tokens=86, server_tool_use=None, service_tier='standard'))\n"
     ]
    }
   ],
   "source": [
    "# Add assistant tool use back to the messages context\n",
    "messages.append({\n",
    "    \"role\": \"assistant\",\n",
    "    \"content\": response.content\n",
    "})\n",
    "\n",
    "tool_result = functions_map[response.content[0].name](\n",
    "    **response.content[0].input\n",
    ")\n",
    "\n",
    "print(\"TOOL_RESULT:\")\n",
    "print(tool_result)\n",
    "\n",
    "tool_content = []\n",
    "tool_result_items = []\n",
    "\n",
    "for data in tool_result:\n",
    "    tool_result_items.append({\n",
    "        \"type\": \"text\",\n",
    "        \"text\": data\n",
    "    })\n",
    "\n",
    "tool_content.append(\n",
    "    {\n",
    "        \"type\": \"tool_result\",\n",
    "        \"tool_use_id\": response.content[0].id,\n",
    "        \"content\": str(tool_result_items),\n",
    "    }\n",
    ")\n",
    "messages.append(\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": tool_content,\n",
    "    }\n",
    ")\n",
    "\n",
    "print(\"SENT MESSAGES\")\n",
    "print(messages)\n",
    "\n",
    "response = client.messages.create(\n",
    "    model=model,\n",
    "    max_tokens=1000,\n",
    "    temperature=1,\n",
    "    tools=tools,\n",
    "    messages=messages\n",
    ")\n",
    "print(\"RESPONSE:\")\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The response we get here may be the final response or model may want us to call the same tool again or another tool. If we need to call the same or another tool, we'll need to wrap this in a while loop until a final response is returned. We'll do this and define a second tool in the next section.\n",
    "\n",
    "## Agentic Workflow\n",
    "\n",
    "In an agentic workflow, the LLM can manage multiple tools in a multi-turn chat scenario.\n",
    "\n",
    "### Setup second tool\n",
    "\n",
    "First, we'll define a new tool to do a semantic search over a Pinecone index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tool 2: Vector Search\n",
    "def pinecone_vector_search(query: str) -> list[dict]:\n",
    "    # search_records embeds and queries the Pinecone index and reranks results in one step\n",
    "    results = index.search(\n",
    "        namespace=\"arxiv\", \n",
    "        query={\n",
    "            # specifies number of results to return\n",
    "            \"top_k\":10,\n",
    "            # specifies the query to embed and search for\n",
    "            \"inputs\":{\n",
    "                \"text\": query\n",
    "            }\n",
    "        },\n",
    "        rerank={\n",
    "            \"model\": \"bge-reranker-v2-m3\",\n",
    "            # reranks the top_k results and returns the top_n\n",
    "            \"top_n\": 5,\n",
    "            \"rank_fields\": [\"chunk_text\"]\n",
    "        },\n",
    "    )\n",
    "\n",
    "    return results[\"result\"][\"hits\"]\n",
    "\n",
    "functions_map = {\n",
    "    \"web_search_engine\": web_search_engine,\n",
    "    \"pinecone_vector_search\": pinecone_vector_search,\n",
    "}\n",
    "\n",
    "pinecone_vector_search_tool = {\n",
    "    \"name\": \"pinecone_vector_search\",\n",
    "    \"description\": \"Searches Pinecone vector database dense index to retrieve documents relevant to the input query. The index contains arxiv papers.\",\n",
    "    \"input_schema\": {\n",
    "        \"type\": \"object\",\n",
    "        \"properties\": {\n",
    "            \"query\": {\n",
    "                \"type\": \"string\",\n",
    "                \"description\": \"The search query.\"\n",
    "            }\n",
    "        },\n",
    "        \"required\": [\"query\"]\n",
    "    }\n",
    "}\n",
    "\n",
    "tools = [\n",
    "    web_search_engine_tool, \n",
    "    pinecone_vector_search_tool,\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the agentic workflow\n",
    "\n",
    "This workflow we defined is looped over to handle multiple tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import anthropic\n",
    "import json\n",
    "\n",
    "client = anthropic.Anthropic()\n",
    "model = \"claude-3-5-haiku-latest\"\n",
    "\n",
    "def run_agent(query, messages=None, verbose_source=False):\n",
    "    \n",
    "    if messages is None:\n",
    "        messages = []\n",
    "        \n",
    "    # Step 1: get user message\n",
    "    print(f\"QUESTION:\\n{query}\")\n",
    "    print(\"=\" * 50)\n",
    "    messages.append({\"role\": \"user\", \"content\": query})\n",
    "    \n",
    "    # Step 2: Generate tool calls (if any)\n",
    "    response = client.messages.create(\n",
    "        model=model,\n",
    "        max_tokens=1000,\n",
    "        temperature=1,\n",
    "        tools=tools,\n",
    "        messages=messages\n",
    "    )\n",
    "\n",
    "    print(\"INITIAL RESPONSE:\")\n",
    "    print(response)\n",
    "    \n",
    "    while response.stop_reason == \"tool_use\":\n",
    "        print(\"TOOL CALLS:\")\n",
    "        print(response.content)        \n",
    "\n",
    "        # Add assistant tool use back to the messages context\n",
    "        messages.append({\n",
    "            \"role\": \"assistant\",\n",
    "            \"content\": response.content\n",
    "        })\n",
    "        \n",
    "        # Step 3: Invoke each tool\n",
    "        for tc in response.content:\n",
    "            if tc.type == \"tool_use\":\n",
    "                print(  \n",
    "                    f\"Tool name: {tc.name} | Parameters: {tc.input}\"\n",
    "                )\n",
    "                print(\"=\" * 50)\n",
    "                # Call the tool with input\n",
    "                tool_result = functions_map[tc.name](\n",
    "                    **tc.input\n",
    "                )\n",
    "\n",
    "                # print(\"TOOL_RESULT\")\n",
    "                # print(tool_result)\n",
    "\n",
    "                tool_content = []\n",
    "                tool_result_items = []\n",
    "\n",
    "                for data in tool_result:\n",
    "                    tool_result_items.append({\n",
    "                        \"type\": \"text\",\n",
    "                        \"text\": data\n",
    "                    })\n",
    "\n",
    "                tool_content.append(\n",
    "                    {\n",
    "                        \"type\": \"tool_result\",\n",
    "                        \"tool_use_id\": tc.id,\n",
    "                        \"content\": str(tool_result_items),\n",
    "                    }\n",
    "                )\n",
    "                messages.append(\n",
    "                    {\n",
    "                        \"role\": \"user\",\n",
    "                        \"content\": tool_content,\n",
    "                    }\n",
    "                )\n",
    "            else:\n",
    "                print(f'Not a tool_use content block: {tc}')\n",
    "   \n",
    "        # Step 4: Generate response\n",
    "        print(\"calling step 4 with:\")\n",
    "        print(messages)\n",
    "        response = client.messages.create(\n",
    "            model=model,\n",
    "            max_tokens=1000,\n",
    "            temperature=1,\n",
    "            tools=tools,\n",
    "            messages=messages\n",
    "        )\n",
    "        print(\"RESPONSE:\")\n",
    "        print(response)\n",
    "        \n",
    "    messages.append(\n",
    "        {\n",
    "            \"role\": \"assistant\",\n",
    "            \"content\": response.content[0].text,\n",
    "        }\n",
    "    )\n",
    "    \n",
    "    # Print final response\n",
    "    print(\"FINAL RESPONSE:\")\n",
    "    print(response.content[0].text)\n",
    "    print(\"=\" * 50)\n",
    "            \n",
    "    return messages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QUESTION:\n",
      "What papers are about personal information?\n",
      "==================================================\n",
      "INITIAL RESPONSE:\n",
      "Message(id='msg_01PA9ZbFAfa2sv1NimWgBgrK', content=[TextBlock(citations=None, text=\"I'll search for relevant academic papers about personal information using the Pinecone vector search.\", type='text'), ToolUseBlock(id='toolu_01QtZPpAzu66MD5cTB7LKAiN', input={'query': 'personal information privacy research papers'}, name='pinecone_vector_search', type='tool_use')], model='claude-3-5-haiku-20241022', role='assistant', stop_reason='tool_use', stop_sequence=None, type='message', usage=Usage(cache_creation_input_tokens=0, cache_read_input_tokens=0, input_tokens=434, output_tokens=81, server_tool_use=None, service_tier='standard'))\n",
      "TOOL CALLS:\n",
      "[TextBlock(citations=None, text=\"I'll search for relevant academic papers about personal information using the Pinecone vector search.\", type='text'), ToolUseBlock(id='toolu_01QtZPpAzu66MD5cTB7LKAiN', input={'query': 'personal information privacy research papers'}, name='pinecone_vector_search', type='tool_use')]\n",
      "Not a tool_use content block: TextBlock(citations=None, text=\"I'll search for relevant academic papers about personal information using the Pinecone vector search.\", type='text')\n",
      "Tool name: pinecone_vector_search | Parameters: {'query': 'personal information privacy research papers'}\n",
      "==================================================\n",
      "calling step 4 with:\n",
      "[{'role': 'user', 'content': 'What papers are about personal information?'}, {'role': 'assistant', 'content': [TextBlock(citations=None, text=\"I'll search for relevant academic papers about personal information using the Pinecone vector search.\", type='text'), ToolUseBlock(id='toolu_01QtZPpAzu66MD5cTB7LKAiN', input={'query': 'personal information privacy research papers'}, name='pinecone_vector_search', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01QtZPpAzu66MD5cTB7LKAiN', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2202.00443\\',\\n \\'_score\\': 0.25552940368652344,\\n \\'fields\\': {\\'authors\\': \\'Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi \\'\\n                       \\'Papadopoulou, David Sánchez, Montserrat Batet\\',\\n            \\'categories\\': \\'cs.CL, cs.AI\\',\\n            \\'chunk_text\\': \\'We present a novel benchmark and associated \\'\\n                          \\'evaluation metrics for assessing\\\\n\\'\\n                          \\'the performance of text anonymization methods. Text \\'\\n                          \\'anonymization, defined as\\\\n\\'\\n                          \\'the task of editing a text document to prevent the \\'\\n                          \\'disclosure of personal\\\\n\\'\\n                          \\'information, currently suffers from a shortage of \\'\\n                          \\'privacy-oriented annotated\\\\n\\'\\n                          \\'text resources, making it difficult to properly \\'\\n                          \\'evaluate the level of privacy\\\\n\\'\\n                          \\'protection offered by various anonymization \\'\\n                          \\'methods. This paper presents TAB\\\\n\\'\\n                          \\'(Text Anonymization Benchmark), a new, open-source \\'\\n                          \\'annotated corpus developed\\\\n\\'\\n                          \\'to address this shortage. The corpus comprises \\'\\n                          \\'1,268 English-language court\\\\n\\'\\n                          \\'cases from the European Court of Human Rights \\'\\n                          \\'(ECHR) enriched with\\\\n\\'\\n                          \\'comprehensive annotations about the personal \\'\\n                          \\'information appearing in each\\\\n\\'\\n                          \\'document, including their semantic category, \\'\\n                          \\'identifier type, confidential\\\\n\\'\\n                          \\'attributes, and co-reference relations. Compared to \\'\\n                          \\'previous work, the TAB\\\\n\\'\\n                          \\'corpus is designed to go beyond traditional \\'\\n                          \\'de-identification (which is limited\\\\n\\'\\n                          \\'to the detection of predefined semantic \\'\\n                          \\'categories), and explicitly marks which\\\\n\\'\\n                          \\'text spans ought to be masked in order to conceal \\'\\n                          \\'the identity of the person to\\\\n\\'\\n                          \\'be protected. Along with presenting the corpus and \\'\\n                          \\'its annotation layers, we\\\\n\\'\\n                          \\'also propose a set of evaluation metrics that are \\'\\n                          \\'specifically tailored towards\\\\n\\'\\n                          \\'measuring the performance of text anonymization, \\'\\n                          \\'both in terms of privacy\\\\n\\'\\n                          \\'protection and utility preservation. We illustrate \\'\\n                          \\'the use of the benchmark and\\\\n\\'\\n                          \\'the proposed metrics by assessing the empirical \\'\\n                          \\'performance of several baseline\\\\n\\'\\n                          \\'text anonymization models. The full corpus along \\'\\n                          \\'with its privacy-oriented\\\\n\\'\\n                          \\'annotation guidelines, evaluation scripts and \\'\\n                          \\'baseline models are available on:\\\\n\\'\\n                          \\'https://github.com/NorskRegnesentral/text-anonymisation-benchmark\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20220125\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2202.00443\\',\\n            \\'title\\': \\'The Text Anonymization Benchmark (TAB): A Dedicated \\'\\n                     \\'Corpus and Evaluation Framework for Text Anonymization\\',\\n            \\'updated\\': \\'20220701\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2205.12628\\',\\n \\'_score\\': 0.12137297540903091,\\n \\'fields\\': {\\'authors\\': \\'Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang\\',\\n            \\'categories\\': \\'cs.CL, cs.AI, cs.CR\\',\\n            \\'chunk_text\\': \\'Are Large Pre-Trained Language Models Leaking Your \\'\\n                          \\'Personal Information? In\\\\n\\'\\n                          \\'this paper, we analyze whether Pre-Trained Language \\'\\n                          \\'Models (PLMs) are prone to\\\\n\\'\\n                          \\'leaking personal information. Specifically, we \\'\\n                          \\'query PLMs for email addresses\\\\n\\'\\n                          \\'with contexts of the email address or prompts \\'\\n                          \"containing the owner\\'s name. We\\\\n\"\\n                          \\'find that PLMs do leak personal information due to \\'\\n                          \\'memorization. However, since\\\\n\\'\\n                          \\'the models are weak at association, the risk of \\'\\n                          \\'specific personal information\\\\n\\'\\n                          \\'being extracted by attackers is low. We hope this \\'\\n                          \\'work could help the community\\\\n\\'\\n                          \\'to better understand the privacy risk of PLMs and \\'\\n                          \\'bring new insights to make\\\\n\\'\\n                          \\'PLMs safe.\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20220525\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2205.12628\\',\\n            \\'title\\': \\'Are Large Pre-Trained Language Models Leaking Your \\'\\n                     \\'Personal Information?\\',\\n            \\'updated\\': \\'20221020\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2302.03262\\',\\n \\'_score\\': 0.10176784545183182,\\n \\'fields\\': {\\'authors\\': \\'Tomoya Matsumoto, Takayuki Miura, Naoto Yanai\\',\\n            \\'categories\\': \\'cs.CR, cs.LG\\',\\n            \\'chunk_text\\': \\'Diffusion models have attracted attention in recent \\'\\n                          \\'years as innovative\\\\n\\'\\n                          \\'generative models. In this paper, we investigate \\'\\n                          \\'whether a diffusion model is\\\\n\\'\\n                          \\'resistant to a membership inference attack, which \\'\\n                          \\'evaluates the privacy leakage\\\\n\\'\\n                          \\'of a machine learning model. We primarily discuss \\'\\n                          \\'the diffusion model from the\\\\n\\'\\n                          \\'standpoints of comparison with a generative \\'\\n                          \\'adversarial network (GAN) as\\\\n\\'\\n                          \\'conventional models and hyperparameters unique to \\'\\n                          \\'the diffusion model, i.e.,\\\\n\\'\\n                          \\'time steps, sampling steps, and sampling variances. \\'\\n                          \\'We conduct extensive\\\\n\\'\\n                          \\'experiments with DDIM as a diffusion model and \\'\\n                          \\'DCGAN as a GAN on the CelebA and\\\\n\\'\\n                          \\'CIFAR-10 datasets in both white-box and black-box \\'\\n                          \\'settings and then confirm if\\\\n\\'\\n                          \\'the diffusion model is comparably resistant to a \\'\\n                          \\'membership inference attack as\\\\n\\'\\n                          \\'GAN. Next, we demonstrate that the impact of time \\'\\n                          \\'steps is significant and\\\\n\\'\\n                          \\'intermediate steps in a noise schedule are the most \\'\\n                          \\'vulnerable to the attack.\\\\n\\'\\n                          \\'We also found two key insights through further \\'\\n                          \\'analysis. First, we identify\\\\n\\'\\n                          \\'that DDIM is vulnerable to the attack for small \\'\\n                          \\'sample sizes instead of\\\\n\\'\\n                          \\'achieving a lower FID. Second, sampling steps in \\'\\n                          \\'hyperparameters are important\\\\n\\'\\n                          \\'for resistance to the attack, whereas the impact of \\'\\n                          \\'sampling variances is quite\\\\n\\'\\n                          \\'limited.\\',\\n            \\'primary_category\\': \\'cs.CR\\',\\n            \\'published\\': \\'20230207\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2302.03262\\',\\n            \\'title\\': \\'Membership Inference Attacks against Diffusion Models\\',\\n            \\'updated\\': \\'20230322\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2209.02299\\',\\n \\'_score\\': 0.08021324872970581,\\n \\'fields\\': {\\'authors\\': \\'Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, \\'\\n                       \\'Alan Wee-Chung Liew, Hongzhi Yin, Quoc Viet Hung \\'\\n                       \\'Nguyen\\',\\n            \\'categories\\': \\'cs.LG, cs.AI\\',\\n            \\'chunk_text\\': \\'Today, computer systems hold large amounts of \\'\\n                          \\'personal data. Yet while such\\\\n\\'\\n                          \\'an abundance of data allows breakthroughs in \\'\\n                          \\'artificial intelligence, and\\\\n\\'\\n                          \\'especially machine learning (ML), its existence can \\'\\n                          \\'be a threat to user\\\\n\\'\\n                          \\'privacy, and it can weaken the bonds of trust \\'\\n                          \\'between humans and AI. Recent\\\\n\\'\\n                          \\'regulations now require that, on request, private \\'\\n                          \\'information about a user must\\\\n\\'\\n                          \\'be removed from both computer systems and from ML \\'\\n                          \\'models, i.e. ``the right to\\\\n\\'\\n                          \"be forgotten\\'\\'). While removing data from back-end \"\\n                          \\'databases should be\\\\n\\'\\n                          \\'straightforward, it is not sufficient in the AI \\'\\n                          \\'context as ML models often\\\\n\\'\\n                          \"`remember\\' the old data. Contemporary adversarial \"\\n                          \\'attacks on trained models\\\\n\\'\\n                          \\'have proven that we can learn whether an instance \\'\\n                          \\'or an attribute belonged to\\\\n\\'\\n                          \\'the training data. This phenomenon calls for a new \\'\\n                          \\'paradigm, namely machine\\\\n\\'\\n                          \\'unlearning, to make ML models forget about \\'\\n                          \\'particular data. It turns out that\\\\n\\'\\n                          \\'recent works on machine unlearning have not been \\'\\n                          \\'able to completely solve the\\\\n\\'\\n                          \\'problem due to the lack of common frameworks and \\'\\n                          \\'resources. Therefore, this\\\\n\\'\\n                          \\'paper aspires to present a comprehensive \\'\\n                          \"examination of machine unlearning\\'s\\\\n\"\\n                          \\'concepts, scenarios, methods, and applications. \\'\\n                          \\'Specifically, as a category\\\\n\\'\\n                          \\'collection of cutting-edge studies, the intention \\'\\n                          \\'behind this article is to\\\\n\\'\\n                          \\'serve as a comprehensive resource for researchers \\'\\n                          \\'and practitioners seeking an\\\\n\\'\\n                          \\'introduction to machine unlearning and its \\'\\n                          \\'formulations, design criteria,\\\\n\\'\\n                          \\'removal requests, algorithms, and applications. In \\'\\n                          \\'addition, we aim to\\\\n\\'\\n                          \\'highlight the key findings, current trends, and new \\'\\n                          \\'research areas that have\\\\n\\'\\n                          \\'not yet featured the use of machine unlearning but \\'\\n                          \\'could benefit greatly from\\\\n\\'\\n                          \\'it. We hope this survey serves as a valuable \\'\\n                          \\'resource for ML researchers and\\\\n\\'\\n                          \\'those seeking to innovate privacy technologies. Our \\'\\n                          \\'resources are publicly\\\\n\\'\\n                          \\'available at \\'\\n                          \\'https://github.com/tamlhp/awesome-machine-unlearning.\\',\\n            \\'primary_category\\': \\'cs.LG\\',\\n            \\'published\\': \\'20220906\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2209.02299\\',\\n            \\'title\\': \\'A Survey of Machine Unlearning\\',\\n            \\'updated\\': \\'20221021\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2301.09956\\',\\n \\'_score\\': 0.06313489377498627,\\n \\'fields\\': {\\'authors\\': \\'Hailong Hu, Jun Pang\\',\\n            \\'categories\\': \\'cs.CR, cs.LG\\',\\n            \\'chunk_text\\': \\'Recent years have witnessed the tremendous success \\'\\n                          \\'of diffusion models in\\\\n\\'\\n                          \\'data synthesis. However, when diffusion models are \\'\\n                          \\'applied to sensitive data,\\\\n\\'\\n                          \\'they also give rise to severe privacy concerns. In \\'\\n                          \\'this paper, we\\\\n\\'\\n                          \\'systematically present the first study about \\'\\n                          \\'membership inference attacks\\\\n\\'\\n                          \\'against diffusion models, which aims to infer \\'\\n                          \\'whether a sample was used to\\\\n\\'\\n                          \\'train the model. Two attack methods are proposed, \\'\\n                          \\'namely loss-based and\\\\n\\'\\n                          \\'likelihood-based attacks. Our attack methods are \\'\\n                          \\'evaluated on several\\\\n\\'\\n                          \\'state-of-the-art diffusion models, over different \\'\\n                          \\'datasets in relation to\\\\n\\'\\n                          \\'privacy-sensitive data. Extensive experimental \\'\\n                          \\'evaluations show that our\\\\n\\'\\n                          \\'attacks can achieve remarkable performance. \\'\\n                          \\'Furthermore, we exhaustively\\\\n\\'\\n                          \\'investigate various factors which can affect attack \\'\\n                          \\'performance. Finally, we\\\\n\\'\\n                          \\'also evaluate the performance of our attack methods \\'\\n                          \\'on diffusion models trained\\\\n\\'\\n                          \\'with differential privacy.\\',\\n            \\'primary_category\\': \\'cs.CR\\',\\n            \\'published\\': \\'20230124\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2301.09956\\',\\n            \\'title\\': \\'Membership Inference of Diffusion Models\\',\\n            \\'updated\\': \\'20230124\\'}}}]'}]}]\n",
      "RESPONSE:\n",
      "Message(id='msg_016LQbAzFLGmkaEERv9LBZ5R', content=[TextBlock(citations=None, text='Based on the search results, I can highlight several interesting papers about personal information:\\n\\n1. \"The Text Anonymization Benchmark (TAB)\" (2022)\\n- This paper presents a benchmark for text anonymization\\n- Focuses on protecting personal information in text documents\\n- Developed using 1,268 court cases from the European Court of Human Rights\\n- Aims to improve methods for concealing personal identities in documents\\n\\n2. \"Are Large Pre-Trained Language Models Leaking Your Personal Information?\" (2022)\\n- Investigates whether large language models can inadvertently leak personal information\\n- Explores potential privacy risks in AI language models\\n- Found that while models can memorize personal information, the risk of extraction is relatively low\\n\\n3. \"A Survey of Machine Unlearning\" (2022)\\n- Examines the concept of \"the right to be forgotten\" in machine learning\\n- Discusses how personal data can be removed from computer systems and AI models\\n- Highlights the challenges of completely erasing personal information from trained models\\n\\n4. \"Membership Inference of Diffusion Models\" (2023)\\n- Investigates privacy concerns in AI models, specifically diffusion models\\n- Explores techniques to determine if a specific piece of data was used in training a model\\n- Focuses on protecting sensitive personal information in machine learning contexts\\n\\nThese papers demonstrate the growing academic interest in protecting personal information, particularly in the context of artificial intelligence, machine learning, and data privacy. They cover various aspects such as text anonymization, model privacy, and methods to prevent unintended personal data exposure.\\n\\nWould you like me to elaborate on any of these papers or search for more specific information about personal information research?', type='text')], model='claude-3-5-haiku-20241022', role='assistant', stop_reason='end_turn', stop_sequence=None, type='message', usage=Usage(cache_creation_input_tokens=0, cache_read_input_tokens=0, input_tokens=3708, output_tokens=370, server_tool_use=None, service_tier='standard'))\n",
      "FINAL RESPONSE:\n",
      "Based on the search results, I can highlight several interesting papers about personal information:\n",
      "\n",
      "1. \"The Text Anonymization Benchmark (TAB)\" (2022)\n",
      "- This paper presents a benchmark for text anonymization\n",
      "- Focuses on protecting personal information in text documents\n",
      "- Developed using 1,268 court cases from the European Court of Human Rights\n",
      "- Aims to improve methods for concealing personal identities in documents\n",
      "\n",
      "2. \"Are Large Pre-Trained Language Models Leaking Your Personal Information?\" (2022)\n",
      "- Investigates whether large language models can inadvertently leak personal information\n",
      "- Explores potential privacy risks in AI language models\n",
      "- Found that while models can memorize personal information, the risk of extraction is relatively low\n",
      "\n",
      "3. \"A Survey of Machine Unlearning\" (2022)\n",
      "- Examines the concept of \"the right to be forgotten\" in machine learning\n",
      "- Discusses how personal data can be removed from computer systems and AI models\n",
      "- Highlights the challenges of completely erasing personal information from trained models\n",
      "\n",
      "4. \"Membership Inference of Diffusion Models\" (2023)\n",
      "- Investigates privacy concerns in AI models, specifically diffusion models\n",
      "- Explores techniques to determine if a specific piece of data was used in training a model\n",
      "- Focuses on protecting sensitive personal information in machine learning contexts\n",
      "\n",
      "These papers demonstrate the growing academic interest in protecting personal information, particularly in the context of artificial intelligence, machine learning, and data privacy. They cover various aspects such as text anonymization, model privacy, and methods to prevent unintended personal data exposure.\n",
      "\n",
      "Would you like me to elaborate on any of these papers or search for more specific information about personal information research?\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "messages = run_agent(\"What papers are about personal information?\", messages=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QUESTION:\n",
      "Are any of these written by Loubna Ben Allal?\n",
      "==================================================\n",
      "INITIAL RESPONSE:\n",
      "Message(id='msg_01CLXHz1hxMtznAomZRUhCEG', content=[TextBlock(citations=None, text=\"I'll search to check if Loubna Ben Allal is an author on any of these papers.\", type='text'), ToolUseBlock(id='toolu_01SifprLfnGMkZtYAEeKvQxH', input={'query': 'Loubna Ben Allal author personal information papers'}, name='web_search_engine', type='tool_use')], model='claude-3-5-haiku-20241022', role='assistant', stop_reason='tool_use', stop_sequence=None, type='message', usage=Usage(cache_creation_input_tokens=0, cache_read_input_tokens=0, input_tokens=4096, output_tokens=90, server_tool_use=None, service_tier='standard'))\n",
      "TOOL CALLS:\n",
      "[TextBlock(citations=None, text=\"I'll search to check if Loubna Ben Allal is an author on any of these papers.\", type='text'), ToolUseBlock(id='toolu_01SifprLfnGMkZtYAEeKvQxH', input={'query': 'Loubna Ben Allal author personal information papers'}, name='web_search_engine', type='tool_use')]\n",
      "Not a tool_use content block: TextBlock(citations=None, text=\"I'll search to check if Loubna Ben Allal is an author on any of these papers.\", type='text')\n",
      "Tool name: web_search_engine | Parameters: {'query': 'Loubna Ben Allal author personal information papers'}\n",
      "==================================================\n",
      "calling step 4 with:\n",
      "[{'role': 'user', 'content': 'What papers are about personal information?'}, {'role': 'assistant', 'content': [TextBlock(citations=None, text=\"I'll search for relevant academic papers about personal information using the Pinecone vector search.\", type='text'), ToolUseBlock(id='toolu_01QtZPpAzu66MD5cTB7LKAiN', input={'query': 'personal information privacy research papers'}, name='pinecone_vector_search', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01QtZPpAzu66MD5cTB7LKAiN', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2202.00443\\',\\n \\'_score\\': 0.25552940368652344,\\n \\'fields\\': {\\'authors\\': \\'Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi \\'\\n                       \\'Papadopoulou, David Sánchez, Montserrat Batet\\',\\n            \\'categories\\': \\'cs.CL, cs.AI\\',\\n            \\'chunk_text\\': \\'We present a novel benchmark and associated \\'\\n                          \\'evaluation metrics for assessing\\\\n\\'\\n                          \\'the performance of text anonymization methods. Text \\'\\n                          \\'anonymization, defined as\\\\n\\'\\n                          \\'the task of editing a text document to prevent the \\'\\n                          \\'disclosure of personal\\\\n\\'\\n                          \\'information, currently suffers from a shortage of \\'\\n                          \\'privacy-oriented annotated\\\\n\\'\\n                          \\'text resources, making it difficult to properly \\'\\n                          \\'evaluate the level of privacy\\\\n\\'\\n                          \\'protection offered by various anonymization \\'\\n                          \\'methods. This paper presents TAB\\\\n\\'\\n                          \\'(Text Anonymization Benchmark), a new, open-source \\'\\n                          \\'annotated corpus developed\\\\n\\'\\n                          \\'to address this shortage. The corpus comprises \\'\\n                          \\'1,268 English-language court\\\\n\\'\\n                          \\'cases from the European Court of Human Rights \\'\\n                          \\'(ECHR) enriched with\\\\n\\'\\n                          \\'comprehensive annotations about the personal \\'\\n                          \\'information appearing in each\\\\n\\'\\n                          \\'document, including their semantic category, \\'\\n                          \\'identifier type, confidential\\\\n\\'\\n                          \\'attributes, and co-reference relations. Compared to \\'\\n                          \\'previous work, the TAB\\\\n\\'\\n                          \\'corpus is designed to go beyond traditional \\'\\n                          \\'de-identification (which is limited\\\\n\\'\\n                          \\'to the detection of predefined semantic \\'\\n                          \\'categories), and explicitly marks which\\\\n\\'\\n                          \\'text spans ought to be masked in order to conceal \\'\\n                          \\'the identity of the person to\\\\n\\'\\n                          \\'be protected. Along with presenting the corpus and \\'\\n                          \\'its annotation layers, we\\\\n\\'\\n                          \\'also propose a set of evaluation metrics that are \\'\\n                          \\'specifically tailored towards\\\\n\\'\\n                          \\'measuring the performance of text anonymization, \\'\\n                          \\'both in terms of privacy\\\\n\\'\\n                          \\'protection and utility preservation. We illustrate \\'\\n                          \\'the use of the benchmark and\\\\n\\'\\n                          \\'the proposed metrics by assessing the empirical \\'\\n                          \\'performance of several baseline\\\\n\\'\\n                          \\'text anonymization models. The full corpus along \\'\\n                          \\'with its privacy-oriented\\\\n\\'\\n                          \\'annotation guidelines, evaluation scripts and \\'\\n                          \\'baseline models are available on:\\\\n\\'\\n                          \\'https://github.com/NorskRegnesentral/text-anonymisation-benchmark\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20220125\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2202.00443\\',\\n            \\'title\\': \\'The Text Anonymization Benchmark (TAB): A Dedicated \\'\\n                     \\'Corpus and Evaluation Framework for Text Anonymization\\',\\n            \\'updated\\': \\'20220701\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2205.12628\\',\\n \\'_score\\': 0.12137297540903091,\\n \\'fields\\': {\\'authors\\': \\'Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang\\',\\n            \\'categories\\': \\'cs.CL, cs.AI, cs.CR\\',\\n            \\'chunk_text\\': \\'Are Large Pre-Trained Language Models Leaking Your \\'\\n                          \\'Personal Information? In\\\\n\\'\\n                          \\'this paper, we analyze whether Pre-Trained Language \\'\\n                          \\'Models (PLMs) are prone to\\\\n\\'\\n                          \\'leaking personal information. Specifically, we \\'\\n                          \\'query PLMs for email addresses\\\\n\\'\\n                          \\'with contexts of the email address or prompts \\'\\n                          \"containing the owner\\'s name. We\\\\n\"\\n                          \\'find that PLMs do leak personal information due to \\'\\n                          \\'memorization. However, since\\\\n\\'\\n                          \\'the models are weak at association, the risk of \\'\\n                          \\'specific personal information\\\\n\\'\\n                          \\'being extracted by attackers is low. We hope this \\'\\n                          \\'work could help the community\\\\n\\'\\n                          \\'to better understand the privacy risk of PLMs and \\'\\n                          \\'bring new insights to make\\\\n\\'\\n                          \\'PLMs safe.\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20220525\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2205.12628\\',\\n            \\'title\\': \\'Are Large Pre-Trained Language Models Leaking Your \\'\\n                     \\'Personal Information?\\',\\n            \\'updated\\': \\'20221020\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2302.03262\\',\\n \\'_score\\': 0.10176784545183182,\\n \\'fields\\': {\\'authors\\': \\'Tomoya Matsumoto, Takayuki Miura, Naoto Yanai\\',\\n            \\'categories\\': \\'cs.CR, cs.LG\\',\\n            \\'chunk_text\\': \\'Diffusion models have attracted attention in recent \\'\\n                          \\'years as innovative\\\\n\\'\\n                          \\'generative models. In this paper, we investigate \\'\\n                          \\'whether a diffusion model is\\\\n\\'\\n                          \\'resistant to a membership inference attack, which \\'\\n                          \\'evaluates the privacy leakage\\\\n\\'\\n                          \\'of a machine learning model. We primarily discuss \\'\\n                          \\'the diffusion model from the\\\\n\\'\\n                          \\'standpoints of comparison with a generative \\'\\n                          \\'adversarial network (GAN) as\\\\n\\'\\n                          \\'conventional models and hyperparameters unique to \\'\\n                          \\'the diffusion model, i.e.,\\\\n\\'\\n                          \\'time steps, sampling steps, and sampling variances. \\'\\n                          \\'We conduct extensive\\\\n\\'\\n                          \\'experiments with DDIM as a diffusion model and \\'\\n                          \\'DCGAN as a GAN on the CelebA and\\\\n\\'\\n                          \\'CIFAR-10 datasets in both white-box and black-box \\'\\n                          \\'settings and then confirm if\\\\n\\'\\n                          \\'the diffusion model is comparably resistant to a \\'\\n                          \\'membership inference attack as\\\\n\\'\\n                          \\'GAN. Next, we demonstrate that the impact of time \\'\\n                          \\'steps is significant and\\\\n\\'\\n                          \\'intermediate steps in a noise schedule are the most \\'\\n                          \\'vulnerable to the attack.\\\\n\\'\\n                          \\'We also found two key insights through further \\'\\n                          \\'analysis. First, we identify\\\\n\\'\\n                          \\'that DDIM is vulnerable to the attack for small \\'\\n                          \\'sample sizes instead of\\\\n\\'\\n                          \\'achieving a lower FID. Second, sampling steps in \\'\\n                          \\'hyperparameters are important\\\\n\\'\\n                          \\'for resistance to the attack, whereas the impact of \\'\\n                          \\'sampling variances is quite\\\\n\\'\\n                          \\'limited.\\',\\n            \\'primary_category\\': \\'cs.CR\\',\\n            \\'published\\': \\'20230207\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2302.03262\\',\\n            \\'title\\': \\'Membership Inference Attacks against Diffusion Models\\',\\n            \\'updated\\': \\'20230322\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2209.02299\\',\\n \\'_score\\': 0.08021324872970581,\\n \\'fields\\': {\\'authors\\': \\'Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, \\'\\n                       \\'Alan Wee-Chung Liew, Hongzhi Yin, Quoc Viet Hung \\'\\n                       \\'Nguyen\\',\\n            \\'categories\\': \\'cs.LG, cs.AI\\',\\n            \\'chunk_text\\': \\'Today, computer systems hold large amounts of \\'\\n                          \\'personal data. Yet while such\\\\n\\'\\n                          \\'an abundance of data allows breakthroughs in \\'\\n                          \\'artificial intelligence, and\\\\n\\'\\n                          \\'especially machine learning (ML), its existence can \\'\\n                          \\'be a threat to user\\\\n\\'\\n                          \\'privacy, and it can weaken the bonds of trust \\'\\n                          \\'between humans and AI. Recent\\\\n\\'\\n                          \\'regulations now require that, on request, private \\'\\n                          \\'information about a user must\\\\n\\'\\n                          \\'be removed from both computer systems and from ML \\'\\n                          \\'models, i.e. ``the right to\\\\n\\'\\n                          \"be forgotten\\'\\'). While removing data from back-end \"\\n                          \\'databases should be\\\\n\\'\\n                          \\'straightforward, it is not sufficient in the AI \\'\\n                          \\'context as ML models often\\\\n\\'\\n                          \"`remember\\' the old data. Contemporary adversarial \"\\n                          \\'attacks on trained models\\\\n\\'\\n                          \\'have proven that we can learn whether an instance \\'\\n                          \\'or an attribute belonged to\\\\n\\'\\n                          \\'the training data. This phenomenon calls for a new \\'\\n                          \\'paradigm, namely machine\\\\n\\'\\n                          \\'unlearning, to make ML models forget about \\'\\n                          \\'particular data. It turns out that\\\\n\\'\\n                          \\'recent works on machine unlearning have not been \\'\\n                          \\'able to completely solve the\\\\n\\'\\n                          \\'problem due to the lack of common frameworks and \\'\\n                          \\'resources. Therefore, this\\\\n\\'\\n                          \\'paper aspires to present a comprehensive \\'\\n                          \"examination of machine unlearning\\'s\\\\n\"\\n                          \\'concepts, scenarios, methods, and applications. \\'\\n                          \\'Specifically, as a category\\\\n\\'\\n                          \\'collection of cutting-edge studies, the intention \\'\\n                          \\'behind this article is to\\\\n\\'\\n                          \\'serve as a comprehensive resource for researchers \\'\\n                          \\'and practitioners seeking an\\\\n\\'\\n                          \\'introduction to machine unlearning and its \\'\\n                          \\'formulations, design criteria,\\\\n\\'\\n                          \\'removal requests, algorithms, and applications. In \\'\\n                          \\'addition, we aim to\\\\n\\'\\n                          \\'highlight the key findings, current trends, and new \\'\\n                          \\'research areas that have\\\\n\\'\\n                          \\'not yet featured the use of machine unlearning but \\'\\n                          \\'could benefit greatly from\\\\n\\'\\n                          \\'it. We hope this survey serves as a valuable \\'\\n                          \\'resource for ML researchers and\\\\n\\'\\n                          \\'those seeking to innovate privacy technologies. Our \\'\\n                          \\'resources are publicly\\\\n\\'\\n                          \\'available at \\'\\n                          \\'https://github.com/tamlhp/awesome-machine-unlearning.\\',\\n            \\'primary_category\\': \\'cs.LG\\',\\n            \\'published\\': \\'20220906\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2209.02299\\',\\n            \\'title\\': \\'A Survey of Machine Unlearning\\',\\n            \\'updated\\': \\'20221021\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2301.09956\\',\\n \\'_score\\': 0.06313489377498627,\\n \\'fields\\': {\\'authors\\': \\'Hailong Hu, Jun Pang\\',\\n            \\'categories\\': \\'cs.CR, cs.LG\\',\\n            \\'chunk_text\\': \\'Recent years have witnessed the tremendous success \\'\\n                          \\'of diffusion models in\\\\n\\'\\n                          \\'data synthesis. However, when diffusion models are \\'\\n                          \\'applied to sensitive data,\\\\n\\'\\n                          \\'they also give rise to severe privacy concerns. In \\'\\n                          \\'this paper, we\\\\n\\'\\n                          \\'systematically present the first study about \\'\\n                          \\'membership inference attacks\\\\n\\'\\n                          \\'against diffusion models, which aims to infer \\'\\n                          \\'whether a sample was used to\\\\n\\'\\n                          \\'train the model. Two attack methods are proposed, \\'\\n                          \\'namely loss-based and\\\\n\\'\\n                          \\'likelihood-based attacks. Our attack methods are \\'\\n                          \\'evaluated on several\\\\n\\'\\n                          \\'state-of-the-art diffusion models, over different \\'\\n                          \\'datasets in relation to\\\\n\\'\\n                          \\'privacy-sensitive data. Extensive experimental \\'\\n                          \\'evaluations show that our\\\\n\\'\\n                          \\'attacks can achieve remarkable performance. \\'\\n                          \\'Furthermore, we exhaustively\\\\n\\'\\n                          \\'investigate various factors which can affect attack \\'\\n                          \\'performance. Finally, we\\\\n\\'\\n                          \\'also evaluate the performance of our attack methods \\'\\n                          \\'on diffusion models trained\\\\n\\'\\n                          \\'with differential privacy.\\',\\n            \\'primary_category\\': \\'cs.CR\\',\\n            \\'published\\': \\'20230124\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2301.09956\\',\\n            \\'title\\': \\'Membership Inference of Diffusion Models\\',\\n            \\'updated\\': \\'20230124\\'}}}]'}]}, {'role': 'assistant', 'content': 'Based on the search results, I can highlight several interesting papers about personal information:\\n\\n1. \"The Text Anonymization Benchmark (TAB)\" (2022)\\n- This paper presents a benchmark for text anonymization\\n- Focuses on protecting personal information in text documents\\n- Developed using 1,268 court cases from the European Court of Human Rights\\n- Aims to improve methods for concealing personal identities in documents\\n\\n2. \"Are Large Pre-Trained Language Models Leaking Your Personal Information?\" (2022)\\n- Investigates whether large language models can inadvertently leak personal information\\n- Explores potential privacy risks in AI language models\\n- Found that while models can memorize personal information, the risk of extraction is relatively low\\n\\n3. \"A Survey of Machine Unlearning\" (2022)\\n- Examines the concept of \"the right to be forgotten\" in machine learning\\n- Discusses how personal data can be removed from computer systems and AI models\\n- Highlights the challenges of completely erasing personal information from trained models\\n\\n4. \"Membership Inference of Diffusion Models\" (2023)\\n- Investigates privacy concerns in AI models, specifically diffusion models\\n- Explores techniques to determine if a specific piece of data was used in training a model\\n- Focuses on protecting sensitive personal information in machine learning contexts\\n\\nThese papers demonstrate the growing academic interest in protecting personal information, particularly in the context of artificial intelligence, machine learning, and data privacy. They cover various aspects such as text anonymization, model privacy, and methods to prevent unintended personal data exposure.\\n\\nWould you like me to elaborate on any of these papers or search for more specific information about personal information research?'}, {'role': 'user', 'content': 'Are any of these written by Loubna Ben Allal?'}, {'role': 'assistant', 'content': [TextBlock(citations=None, text=\"I'll search to check if Loubna Ben Allal is an author on any of these papers.\", type='text'), ToolUseBlock(id='toolu_01SifprLfnGMkZtYAEeKvQxH', input={'query': 'Loubna Ben Allal author personal information papers'}, name='web_search_engine', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01SifprLfnGMkZtYAEeKvQxH', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'LOUBNA BEN ALLAL - Google Scholar\\', \\'href\\': \\'https://scholar.google.com/citations?user=reU1i-sAAAAJ\\', \\'body\\': \\'\\\\u202aHugging Face\\\\u202c - \\\\u202a\\\\u202aCited by 4,518\\\\u202c\\\\u202c - \\\\u202aMachine Learning\\\\u202c\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'Loubna Ben Allal - Home - ACM Digital Library\\', \\'href\\': \\'https://dl.acm.org/profile/99661625996\\', \\'body\\': \"Colleague Collaboration Author\\'s Latest Publications research-article Scaling laws and compute-optimal training beyond fixed training durations Alexander Hägele, Elie Bakouch, Atli Kosson, + 3\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \"[2301.03988] SantaCoder: don\\'t reach for the stars! - arXiv.org\", \\'href\\': \\'https://arxiv.org/abs/2301.03988\\', \\'body\\': \"View a PDF of the paper titled SantaCoder: don\\'t reach for the stars!, by Loubna Ben Allal and 40 other authors\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'loubnabnl (Loubna Ben Allal) - Hugging Face\\', \\'href\\': \\'https://huggingface.co/loubnabnl\\', \\'body\\': \\'Papers11 arxiv:2506.05209 arxiv:2504.05299 arxiv:2502.02737 arxiv:2406.17557 Expand 11 papers spaces6\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'Loubna Ben allal | Papers With Code\\', \\'href\\': \\'https://paperswithcode.com/search?q=author:Loubna+Ben+allal\\', \\'body\\': \\'Papers by Loubna Ben allal with links to code and results.\\'}}]'}]}]\n",
      "RESPONSE:\n",
      "Message(id='msg_01CxPkzEeXHpe1iq1HfQf9MN', content=[TextBlock(citations=None, text=\"I'll do another search to find her specific papers about personal information:\", type='text'), ToolUseBlock(id='toolu_017MGyC5QaPYFGgSRWHe2dmM', input={'query': 'Loubna Ben Allal personal information privacy research'}, name='pinecone_vector_search', type='tool_use')], model='claude-3-5-haiku-20241022', role='assistant', stop_reason='tool_use', stop_sequence=None, type='message', usage=Usage(cache_creation_input_tokens=0, cache_read_input_tokens=0, input_tokens=4714, output_tokens=84, server_tool_use=None, service_tier='standard'))\n",
      "TOOL CALLS:\n",
      "[TextBlock(citations=None, text=\"I'll do another search to find her specific papers about personal information:\", type='text'), ToolUseBlock(id='toolu_017MGyC5QaPYFGgSRWHe2dmM', input={'query': 'Loubna Ben Allal personal information privacy research'}, name='pinecone_vector_search', type='tool_use')]\n",
      "Not a tool_use content block: TextBlock(citations=None, text=\"I'll do another search to find her specific papers about personal information:\", type='text')\n",
      "Tool name: pinecone_vector_search | Parameters: {'query': 'Loubna Ben Allal personal information privacy research'}\n",
      "==================================================\n",
      "calling step 4 with:\n",
      "[{'role': 'user', 'content': 'What papers are about personal information?'}, {'role': 'assistant', 'content': [TextBlock(citations=None, text=\"I'll search for relevant academic papers about personal information using the Pinecone vector search.\", type='text'), ToolUseBlock(id='toolu_01QtZPpAzu66MD5cTB7LKAiN', input={'query': 'personal information privacy research papers'}, name='pinecone_vector_search', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01QtZPpAzu66MD5cTB7LKAiN', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2202.00443\\',\\n \\'_score\\': 0.25552940368652344,\\n \\'fields\\': {\\'authors\\': \\'Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi \\'\\n                       \\'Papadopoulou, David Sánchez, Montserrat Batet\\',\\n            \\'categories\\': \\'cs.CL, cs.AI\\',\\n            \\'chunk_text\\': \\'We present a novel benchmark and associated \\'\\n                          \\'evaluation metrics for assessing\\\\n\\'\\n                          \\'the performance of text anonymization methods. Text \\'\\n                          \\'anonymization, defined as\\\\n\\'\\n                          \\'the task of editing a text document to prevent the \\'\\n                          \\'disclosure of personal\\\\n\\'\\n                          \\'information, currently suffers from a shortage of \\'\\n                          \\'privacy-oriented annotated\\\\n\\'\\n                          \\'text resources, making it difficult to properly \\'\\n                          \\'evaluate the level of privacy\\\\n\\'\\n                          \\'protection offered by various anonymization \\'\\n                          \\'methods. This paper presents TAB\\\\n\\'\\n                          \\'(Text Anonymization Benchmark), a new, open-source \\'\\n                          \\'annotated corpus developed\\\\n\\'\\n                          \\'to address this shortage. The corpus comprises \\'\\n                          \\'1,268 English-language court\\\\n\\'\\n                          \\'cases from the European Court of Human Rights \\'\\n                          \\'(ECHR) enriched with\\\\n\\'\\n                          \\'comprehensive annotations about the personal \\'\\n                          \\'information appearing in each\\\\n\\'\\n                          \\'document, including their semantic category, \\'\\n                          \\'identifier type, confidential\\\\n\\'\\n                          \\'attributes, and co-reference relations. Compared to \\'\\n                          \\'previous work, the TAB\\\\n\\'\\n                          \\'corpus is designed to go beyond traditional \\'\\n                          \\'de-identification (which is limited\\\\n\\'\\n                          \\'to the detection of predefined semantic \\'\\n                          \\'categories), and explicitly marks which\\\\n\\'\\n                          \\'text spans ought to be masked in order to conceal \\'\\n                          \\'the identity of the person to\\\\n\\'\\n                          \\'be protected. Along with presenting the corpus and \\'\\n                          \\'its annotation layers, we\\\\n\\'\\n                          \\'also propose a set of evaluation metrics that are \\'\\n                          \\'specifically tailored towards\\\\n\\'\\n                          \\'measuring the performance of text anonymization, \\'\\n                          \\'both in terms of privacy\\\\n\\'\\n                          \\'protection and utility preservation. We illustrate \\'\\n                          \\'the use of the benchmark and\\\\n\\'\\n                          \\'the proposed metrics by assessing the empirical \\'\\n                          \\'performance of several baseline\\\\n\\'\\n                          \\'text anonymization models. The full corpus along \\'\\n                          \\'with its privacy-oriented\\\\n\\'\\n                          \\'annotation guidelines, evaluation scripts and \\'\\n                          \\'baseline models are available on:\\\\n\\'\\n                          \\'https://github.com/NorskRegnesentral/text-anonymisation-benchmark\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20220125\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2202.00443\\',\\n            \\'title\\': \\'The Text Anonymization Benchmark (TAB): A Dedicated \\'\\n                     \\'Corpus and Evaluation Framework for Text Anonymization\\',\\n            \\'updated\\': \\'20220701\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2205.12628\\',\\n \\'_score\\': 0.12137297540903091,\\n \\'fields\\': {\\'authors\\': \\'Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang\\',\\n            \\'categories\\': \\'cs.CL, cs.AI, cs.CR\\',\\n            \\'chunk_text\\': \\'Are Large Pre-Trained Language Models Leaking Your \\'\\n                          \\'Personal Information? In\\\\n\\'\\n                          \\'this paper, we analyze whether Pre-Trained Language \\'\\n                          \\'Models (PLMs) are prone to\\\\n\\'\\n                          \\'leaking personal information. Specifically, we \\'\\n                          \\'query PLMs for email addresses\\\\n\\'\\n                          \\'with contexts of the email address or prompts \\'\\n                          \"containing the owner\\'s name. We\\\\n\"\\n                          \\'find that PLMs do leak personal information due to \\'\\n                          \\'memorization. However, since\\\\n\\'\\n                          \\'the models are weak at association, the risk of \\'\\n                          \\'specific personal information\\\\n\\'\\n                          \\'being extracted by attackers is low. We hope this \\'\\n                          \\'work could help the community\\\\n\\'\\n                          \\'to better understand the privacy risk of PLMs and \\'\\n                          \\'bring new insights to make\\\\n\\'\\n                          \\'PLMs safe.\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20220525\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2205.12628\\',\\n            \\'title\\': \\'Are Large Pre-Trained Language Models Leaking Your \\'\\n                     \\'Personal Information?\\',\\n            \\'updated\\': \\'20221020\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2302.03262\\',\\n \\'_score\\': 0.10176784545183182,\\n \\'fields\\': {\\'authors\\': \\'Tomoya Matsumoto, Takayuki Miura, Naoto Yanai\\',\\n            \\'categories\\': \\'cs.CR, cs.LG\\',\\n            \\'chunk_text\\': \\'Diffusion models have attracted attention in recent \\'\\n                          \\'years as innovative\\\\n\\'\\n                          \\'generative models. In this paper, we investigate \\'\\n                          \\'whether a diffusion model is\\\\n\\'\\n                          \\'resistant to a membership inference attack, which \\'\\n                          \\'evaluates the privacy leakage\\\\n\\'\\n                          \\'of a machine learning model. We primarily discuss \\'\\n                          \\'the diffusion model from the\\\\n\\'\\n                          \\'standpoints of comparison with a generative \\'\\n                          \\'adversarial network (GAN) as\\\\n\\'\\n                          \\'conventional models and hyperparameters unique to \\'\\n                          \\'the diffusion model, i.e.,\\\\n\\'\\n                          \\'time steps, sampling steps, and sampling variances. \\'\\n                          \\'We conduct extensive\\\\n\\'\\n                          \\'experiments with DDIM as a diffusion model and \\'\\n                          \\'DCGAN as a GAN on the CelebA and\\\\n\\'\\n                          \\'CIFAR-10 datasets in both white-box and black-box \\'\\n                          \\'settings and then confirm if\\\\n\\'\\n                          \\'the diffusion model is comparably resistant to a \\'\\n                          \\'membership inference attack as\\\\n\\'\\n                          \\'GAN. Next, we demonstrate that the impact of time \\'\\n                          \\'steps is significant and\\\\n\\'\\n                          \\'intermediate steps in a noise schedule are the most \\'\\n                          \\'vulnerable to the attack.\\\\n\\'\\n                          \\'We also found two key insights through further \\'\\n                          \\'analysis. First, we identify\\\\n\\'\\n                          \\'that DDIM is vulnerable to the attack for small \\'\\n                          \\'sample sizes instead of\\\\n\\'\\n                          \\'achieving a lower FID. Second, sampling steps in \\'\\n                          \\'hyperparameters are important\\\\n\\'\\n                          \\'for resistance to the attack, whereas the impact of \\'\\n                          \\'sampling variances is quite\\\\n\\'\\n                          \\'limited.\\',\\n            \\'primary_category\\': \\'cs.CR\\',\\n            \\'published\\': \\'20230207\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2302.03262\\',\\n            \\'title\\': \\'Membership Inference Attacks against Diffusion Models\\',\\n            \\'updated\\': \\'20230322\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2209.02299\\',\\n \\'_score\\': 0.08021324872970581,\\n \\'fields\\': {\\'authors\\': \\'Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, \\'\\n                       \\'Alan Wee-Chung Liew, Hongzhi Yin, Quoc Viet Hung \\'\\n                       \\'Nguyen\\',\\n            \\'categories\\': \\'cs.LG, cs.AI\\',\\n            \\'chunk_text\\': \\'Today, computer systems hold large amounts of \\'\\n                          \\'personal data. Yet while such\\\\n\\'\\n                          \\'an abundance of data allows breakthroughs in \\'\\n                          \\'artificial intelligence, and\\\\n\\'\\n                          \\'especially machine learning (ML), its existence can \\'\\n                          \\'be a threat to user\\\\n\\'\\n                          \\'privacy, and it can weaken the bonds of trust \\'\\n                          \\'between humans and AI. Recent\\\\n\\'\\n                          \\'regulations now require that, on request, private \\'\\n                          \\'information about a user must\\\\n\\'\\n                          \\'be removed from both computer systems and from ML \\'\\n                          \\'models, i.e. ``the right to\\\\n\\'\\n                          \"be forgotten\\'\\'). While removing data from back-end \"\\n                          \\'databases should be\\\\n\\'\\n                          \\'straightforward, it is not sufficient in the AI \\'\\n                          \\'context as ML models often\\\\n\\'\\n                          \"`remember\\' the old data. Contemporary adversarial \"\\n                          \\'attacks on trained models\\\\n\\'\\n                          \\'have proven that we can learn whether an instance \\'\\n                          \\'or an attribute belonged to\\\\n\\'\\n                          \\'the training data. This phenomenon calls for a new \\'\\n                          \\'paradigm, namely machine\\\\n\\'\\n                          \\'unlearning, to make ML models forget about \\'\\n                          \\'particular data. It turns out that\\\\n\\'\\n                          \\'recent works on machine unlearning have not been \\'\\n                          \\'able to completely solve the\\\\n\\'\\n                          \\'problem due to the lack of common frameworks and \\'\\n                          \\'resources. Therefore, this\\\\n\\'\\n                          \\'paper aspires to present a comprehensive \\'\\n                          \"examination of machine unlearning\\'s\\\\n\"\\n                          \\'concepts, scenarios, methods, and applications. \\'\\n                          \\'Specifically, as a category\\\\n\\'\\n                          \\'collection of cutting-edge studies, the intention \\'\\n                          \\'behind this article is to\\\\n\\'\\n                          \\'serve as a comprehensive resource for researchers \\'\\n                          \\'and practitioners seeking an\\\\n\\'\\n                          \\'introduction to machine unlearning and its \\'\\n                          \\'formulations, design criteria,\\\\n\\'\\n                          \\'removal requests, algorithms, and applications. In \\'\\n                          \\'addition, we aim to\\\\n\\'\\n                          \\'highlight the key findings, current trends, and new \\'\\n                          \\'research areas that have\\\\n\\'\\n                          \\'not yet featured the use of machine unlearning but \\'\\n                          \\'could benefit greatly from\\\\n\\'\\n                          \\'it. We hope this survey serves as a valuable \\'\\n                          \\'resource for ML researchers and\\\\n\\'\\n                          \\'those seeking to innovate privacy technologies. Our \\'\\n                          \\'resources are publicly\\\\n\\'\\n                          \\'available at \\'\\n                          \\'https://github.com/tamlhp/awesome-machine-unlearning.\\',\\n            \\'primary_category\\': \\'cs.LG\\',\\n            \\'published\\': \\'20220906\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2209.02299\\',\\n            \\'title\\': \\'A Survey of Machine Unlearning\\',\\n            \\'updated\\': \\'20221021\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2301.09956\\',\\n \\'_score\\': 0.06313489377498627,\\n \\'fields\\': {\\'authors\\': \\'Hailong Hu, Jun Pang\\',\\n            \\'categories\\': \\'cs.CR, cs.LG\\',\\n            \\'chunk_text\\': \\'Recent years have witnessed the tremendous success \\'\\n                          \\'of diffusion models in\\\\n\\'\\n                          \\'data synthesis. However, when diffusion models are \\'\\n                          \\'applied to sensitive data,\\\\n\\'\\n                          \\'they also give rise to severe privacy concerns. In \\'\\n                          \\'this paper, we\\\\n\\'\\n                          \\'systematically present the first study about \\'\\n                          \\'membership inference attacks\\\\n\\'\\n                          \\'against diffusion models, which aims to infer \\'\\n                          \\'whether a sample was used to\\\\n\\'\\n                          \\'train the model. Two attack methods are proposed, \\'\\n                          \\'namely loss-based and\\\\n\\'\\n                          \\'likelihood-based attacks. Our attack methods are \\'\\n                          \\'evaluated on several\\\\n\\'\\n                          \\'state-of-the-art diffusion models, over different \\'\\n                          \\'datasets in relation to\\\\n\\'\\n                          \\'privacy-sensitive data. Extensive experimental \\'\\n                          \\'evaluations show that our\\\\n\\'\\n                          \\'attacks can achieve remarkable performance. \\'\\n                          \\'Furthermore, we exhaustively\\\\n\\'\\n                          \\'investigate various factors which can affect attack \\'\\n                          \\'performance. Finally, we\\\\n\\'\\n                          \\'also evaluate the performance of our attack methods \\'\\n                          \\'on diffusion models trained\\\\n\\'\\n                          \\'with differential privacy.\\',\\n            \\'primary_category\\': \\'cs.CR\\',\\n            \\'published\\': \\'20230124\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2301.09956\\',\\n            \\'title\\': \\'Membership Inference of Diffusion Models\\',\\n            \\'updated\\': \\'20230124\\'}}}]'}]}, {'role': 'assistant', 'content': 'Based on the search results, I can highlight several interesting papers about personal information:\\n\\n1. \"The Text Anonymization Benchmark (TAB)\" (2022)\\n- This paper presents a benchmark for text anonymization\\n- Focuses on protecting personal information in text documents\\n- Developed using 1,268 court cases from the European Court of Human Rights\\n- Aims to improve methods for concealing personal identities in documents\\n\\n2. \"Are Large Pre-Trained Language Models Leaking Your Personal Information?\" (2022)\\n- Investigates whether large language models can inadvertently leak personal information\\n- Explores potential privacy risks in AI language models\\n- Found that while models can memorize personal information, the risk of extraction is relatively low\\n\\n3. \"A Survey of Machine Unlearning\" (2022)\\n- Examines the concept of \"the right to be forgotten\" in machine learning\\n- Discusses how personal data can be removed from computer systems and AI models\\n- Highlights the challenges of completely erasing personal information from trained models\\n\\n4. \"Membership Inference of Diffusion Models\" (2023)\\n- Investigates privacy concerns in AI models, specifically diffusion models\\n- Explores techniques to determine if a specific piece of data was used in training a model\\n- Focuses on protecting sensitive personal information in machine learning contexts\\n\\nThese papers demonstrate the growing academic interest in protecting personal information, particularly in the context of artificial intelligence, machine learning, and data privacy. They cover various aspects such as text anonymization, model privacy, and methods to prevent unintended personal data exposure.\\n\\nWould you like me to elaborate on any of these papers or search for more specific information about personal information research?'}, {'role': 'user', 'content': 'Are any of these written by Loubna Ben Allal?'}, {'role': 'assistant', 'content': [TextBlock(citations=None, text=\"I'll search to check if Loubna Ben Allal is an author on any of these papers.\", type='text'), ToolUseBlock(id='toolu_01SifprLfnGMkZtYAEeKvQxH', input={'query': 'Loubna Ben Allal author personal information papers'}, name='web_search_engine', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01SifprLfnGMkZtYAEeKvQxH', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'LOUBNA BEN ALLAL - Google Scholar\\', \\'href\\': \\'https://scholar.google.com/citations?user=reU1i-sAAAAJ\\', \\'body\\': \\'\\\\u202aHugging Face\\\\u202c - \\\\u202a\\\\u202aCited by 4,518\\\\u202c\\\\u202c - \\\\u202aMachine Learning\\\\u202c\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'Loubna Ben Allal - Home - ACM Digital Library\\', \\'href\\': \\'https://dl.acm.org/profile/99661625996\\', \\'body\\': \"Colleague Collaboration Author\\'s Latest Publications research-article Scaling laws and compute-optimal training beyond fixed training durations Alexander Hägele, Elie Bakouch, Atli Kosson, + 3\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \"[2301.03988] SantaCoder: don\\'t reach for the stars! - arXiv.org\", \\'href\\': \\'https://arxiv.org/abs/2301.03988\\', \\'body\\': \"View a PDF of the paper titled SantaCoder: don\\'t reach for the stars!, by Loubna Ben Allal and 40 other authors\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'loubnabnl (Loubna Ben Allal) - Hugging Face\\', \\'href\\': \\'https://huggingface.co/loubnabnl\\', \\'body\\': \\'Papers11 arxiv:2506.05209 arxiv:2504.05299 arxiv:2502.02737 arxiv:2406.17557 Expand 11 papers spaces6\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'Loubna Ben allal | Papers With Code\\', \\'href\\': \\'https://paperswithcode.com/search?q=author:Loubna+Ben+allal\\', \\'body\\': \\'Papers by Loubna Ben allal with links to code and results.\\'}}]'}]}, {'role': 'assistant', 'content': [TextBlock(citations=None, text=\"I'll do another search to find her specific papers about personal information:\", type='text'), ToolUseBlock(id='toolu_017MGyC5QaPYFGgSRWHe2dmM', input={'query': 'Loubna Ben Allal personal information privacy research'}, name='pinecone_vector_search', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_017MGyC5QaPYFGgSRWHe2dmM', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2202.00443\\',\\n \\'_score\\': 0.0022694687359035015,\\n \\'fields\\': {\\'authors\\': \\'Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi \\'\\n                       \\'Papadopoulou, David Sánchez, Montserrat Batet\\',\\n            \\'categories\\': \\'cs.CL, cs.AI\\',\\n            \\'chunk_text\\': \\'We present a novel benchmark and associated \\'\\n                          \\'evaluation metrics for assessing\\\\n\\'\\n                          \\'the performance of text anonymization methods. Text \\'\\n                          \\'anonymization, defined as\\\\n\\'\\n                          \\'the task of editing a text document to prevent the \\'\\n                          \\'disclosure of personal\\\\n\\'\\n                          \\'information, currently suffers from a shortage of \\'\\n                          \\'privacy-oriented annotated\\\\n\\'\\n                          \\'text resources, making it difficult to properly \\'\\n                          \\'evaluate the level of privacy\\\\n\\'\\n                          \\'protection offered by various anonymization \\'\\n                          \\'methods. This paper presents TAB\\\\n\\'\\n                          \\'(Text Anonymization Benchmark), a new, open-source \\'\\n                          \\'annotated corpus developed\\\\n\\'\\n                          \\'to address this shortage. The corpus comprises \\'\\n                          \\'1,268 English-language court\\\\n\\'\\n                          \\'cases from the European Court of Human Rights \\'\\n                          \\'(ECHR) enriched with\\\\n\\'\\n                          \\'comprehensive annotations about the personal \\'\\n                          \\'information appearing in each\\\\n\\'\\n                          \\'document, including their semantic category, \\'\\n                          \\'identifier type, confidential\\\\n\\'\\n                          \\'attributes, and co-reference relations. Compared to \\'\\n                          \\'previous work, the TAB\\\\n\\'\\n                          \\'corpus is designed to go beyond traditional \\'\\n                          \\'de-identification (which is limited\\\\n\\'\\n                          \\'to the detection of predefined semantic \\'\\n                          \\'categories), and explicitly marks which\\\\n\\'\\n                          \\'text spans ought to be masked in order to conceal \\'\\n                          \\'the identity of the person to\\\\n\\'\\n                          \\'be protected. Along with presenting the corpus and \\'\\n                          \\'its annotation layers, we\\\\n\\'\\n                          \\'also propose a set of evaluation metrics that are \\'\\n                          \\'specifically tailored towards\\\\n\\'\\n                          \\'measuring the performance of text anonymization, \\'\\n                          \\'both in terms of privacy\\\\n\\'\\n                          \\'protection and utility preservation. We illustrate \\'\\n                          \\'the use of the benchmark and\\\\n\\'\\n                          \\'the proposed metrics by assessing the empirical \\'\\n                          \\'performance of several baseline\\\\n\\'\\n                          \\'text anonymization models. The full corpus along \\'\\n                          \\'with its privacy-oriented\\\\n\\'\\n                          \\'annotation guidelines, evaluation scripts and \\'\\n                          \\'baseline models are available on:\\\\n\\'\\n                          \\'https://github.com/NorskRegnesentral/text-anonymisation-benchmark\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20220125\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2202.00443\\',\\n            \\'title\\': \\'The Text Anonymization Benchmark (TAB): A Dedicated \\'\\n                     \\'Corpus and Evaluation Framework for Text Anonymization\\',\\n            \\'updated\\': \\'20220701\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2209.02299\\',\\n \\'_score\\': 0.001674399245530367,\\n \\'fields\\': {\\'authors\\': \\'Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, \\'\\n                       \\'Alan Wee-Chung Liew, Hongzhi Yin, Quoc Viet Hung \\'\\n                       \\'Nguyen\\',\\n            \\'categories\\': \\'cs.LG, cs.AI\\',\\n            \\'chunk_text\\': \\'Today, computer systems hold large amounts of \\'\\n                          \\'personal data. Yet while such\\\\n\\'\\n                          \\'an abundance of data allows breakthroughs in \\'\\n                          \\'artificial intelligence, and\\\\n\\'\\n                          \\'especially machine learning (ML), its existence can \\'\\n                          \\'be a threat to user\\\\n\\'\\n                          \\'privacy, and it can weaken the bonds of trust \\'\\n                          \\'between humans and AI. Recent\\\\n\\'\\n                          \\'regulations now require that, on request, private \\'\\n                          \\'information about a user must\\\\n\\'\\n                          \\'be removed from both computer systems and from ML \\'\\n                          \\'models, i.e. ``the right to\\\\n\\'\\n                          \"be forgotten\\'\\'). While removing data from back-end \"\\n                          \\'databases should be\\\\n\\'\\n                          \\'straightforward, it is not sufficient in the AI \\'\\n                          \\'context as ML models often\\\\n\\'\\n                          \"`remember\\' the old data. Contemporary adversarial \"\\n                          \\'attacks on trained models\\\\n\\'\\n                          \\'have proven that we can learn whether an instance \\'\\n                          \\'or an attribute belonged to\\\\n\\'\\n                          \\'the training data. This phenomenon calls for a new \\'\\n                          \\'paradigm, namely machine\\\\n\\'\\n                          \\'unlearning, to make ML models forget about \\'\\n                          \\'particular data. It turns out that\\\\n\\'\\n                          \\'recent works on machine unlearning have not been \\'\\n                          \\'able to completely solve the\\\\n\\'\\n                          \\'problem due to the lack of common frameworks and \\'\\n                          \\'resources. Therefore, this\\\\n\\'\\n                          \\'paper aspires to present a comprehensive \\'\\n                          \"examination of machine unlearning\\'s\\\\n\"\\n                          \\'concepts, scenarios, methods, and applications. \\'\\n                          \\'Specifically, as a category\\\\n\\'\\n                          \\'collection of cutting-edge studies, the intention \\'\\n                          \\'behind this article is to\\\\n\\'\\n                          \\'serve as a comprehensive resource for researchers \\'\\n                          \\'and practitioners seeking an\\\\n\\'\\n                          \\'introduction to machine unlearning and its \\'\\n                          \\'formulations, design criteria,\\\\n\\'\\n                          \\'removal requests, algorithms, and applications. In \\'\\n                          \\'addition, we aim to\\\\n\\'\\n                          \\'highlight the key findings, current trends, and new \\'\\n                          \\'research areas that have\\\\n\\'\\n                          \\'not yet featured the use of machine unlearning but \\'\\n                          \\'could benefit greatly from\\\\n\\'\\n                          \\'it. We hope this survey serves as a valuable \\'\\n                          \\'resource for ML researchers and\\\\n\\'\\n                          \\'those seeking to innovate privacy technologies. Our \\'\\n                          \\'resources are publicly\\\\n\\'\\n                          \\'available at \\'\\n                          \\'https://github.com/tamlhp/awesome-machine-unlearning.\\',\\n            \\'primary_category\\': \\'cs.LG\\',\\n            \\'published\\': \\'20220906\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2209.02299\\',\\n            \\'title\\': \\'A Survey of Machine Unlearning\\',\\n            \\'updated\\': \\'20221021\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2202.05520\\',\\n \\'_score\\': 0.0010733063099905849,\\n \\'fields\\': {\\'authors\\': \\'Hannah Brown, Katherine Lee, Fatemehsadat \\'\\n                       \\'Mireshghallah, Reza Shokri, Florian Tramèr\\',\\n            \\'categories\\': \\'stat.ML, cs.CL, cs.LG\\',\\n            \\'chunk_text\\': \\'Natural language reflects our private lives and \\'\\n                          \\'identities, making its\\\\n\\'\\n                          \\'privacy concerns as broad as those of real life. \\'\\n                          \\'Language models lack the\\\\n\\'\\n                          \\'ability to understand the context and sensitivity \\'\\n                          \\'of text, and tend to memorize\\\\n\\'\\n                          \\'phrases present in their training sets. An \\'\\n                          \\'adversary can exploit this tendency\\\\n\\'\\n                          \\'to extract training data. Depending on the nature \\'\\n                          \\'of the content and the\\\\n\\'\\n                          \\'context in which this data was collected, this \\'\\n                          \\'could violate expectations of\\\\n\\'\\n                          \\'privacy. Thus there is a growing interest in \\'\\n                          \\'techniques for training language\\\\n\\'\\n                          \\'models that preserve privacy. In this paper, we \\'\\n                          \\'discuss the mismatch between\\\\n\\'\\n                          \\'the narrow assumptions made by popular data \\'\\n                          \\'protection techniques (data\\\\n\\'\\n                          \\'sanitization and differential privacy), and the \\'\\n                          \\'broadness of natural language\\\\n\\'\\n                          \\'and of privacy as a social norm. We argue that \\'\\n                          \\'existing protection methods\\\\n\\'\\n                          \\'cannot guarantee a generic and meaningful notion of \\'\\n                          \\'privacy for language\\\\n\\'\\n                          \\'models. We conclude that language models should be \\'\\n                          \\'trained on text data which\\\\n\\'\\n                          \\'was explicitly produced for public use.\\',\\n            \\'primary_category\\': \\'stat.ML\\',\\n            \\'published\\': \\'20220211\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2202.05520\\',\\n            \\'title\\': \\'What Does it Mean for a Language Model to Preserve \\'\\n                     \\'Privacy?\\',\\n            \\'updated\\': \\'20220214\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2305.02547\\',\\n \\'_score\\': 0.0008559006964787841,\\n \\'fields\\': {\\'authors\\': \\'Hang Jiang, Xiajie Zhang, Xubo Cao, Jad Kabbara\\',\\n            \\'categories\\': \\'cs.CL, cs.AI, cs.HC\\',\\n            \\'chunk_text\\': \\'Despite the many use cases for large language \\'\\n                          \\'models (LLMs) in creating\\\\n\\'\\n                          \\'personalized chatbots, there has been limited \\'\\n                          \\'research on evaluating the extent\\\\n\\'\\n                          \\'to which the behaviors of personalized LLMs \\'\\n                          \\'accurately and consistently reflect\\\\n\\'\\n                          \\'specific personality traits. We consider studying \\'\\n                          \\'the behavior of LLM-based\\\\n\\'\\n                          \\'agents, referred to as LLM personas, and present a \\'\\n                          \\'case study with ChatGPT and\\\\n\\'\\n                          \\'GPT-4. The study investigates whether LLMs can \\'\\n                          \\'generate content that aligns\\\\n\\'\\n                          \\'with their assigned personality profiles. To this \\'\\n                          \\'end, we create distinct LLM\\\\n\\'\\n                          \\'personas based on the Big Five personality model, \\'\\n                          \\'have them complete the\\\\n\\'\\n                          \\'44-item Big Five Inventory (BFI) personality test \\'\\n                          \\'and a story writing task, and\\\\n\\'\\n                          \\'then assess their essays with automatic and human \\'\\n                          \\'evaluations. Results show\\\\n\\'\\n                          \"that LLM personas\\' self-reported BFI scores are \"\\n                          \\'consistent with their\\\\n\\'\\n                          \\'designated personality types, with large effect \\'\\n                          \\'sizes observed across five\\\\n\\'\\n                          \\'traits. Additionally, there are significant \\'\\n                          \\'correlations between the assigned\\\\n\\'\\n                          \\'personality types and certain psycholinguistic \\'\\n                          \\'features of their writings, as\\\\n\\'\\n                          \\'measured by the Linguistic Inquiry and Word Count \\'\\n                          \\'(LIWC) tool. Interestingly,\\\\n\\'\\n                          \\'human evaluators perceive the stories as less \\'\\n                          \\'personal when told that the\\\\n\\'\\n                          \\'stories are authored by AI. However, their \\'\\n                          \\'judgments on other aspects of the\\\\n\\'\\n                          \\'writing such as readability, cohesiveness, \\'\\n                          \\'redundancy, likeability, and\\\\n\\'\\n                          \\'believability remain largely unaffected. Notably, \\'\\n                          \\'when evaluators were informed\\\\n\\'\\n                          \\'about the AI authorship, their accuracy in \\'\\n                          \\'identifying the intended personality\\\\n\\'\\n                          \\'traits from the stories decreased by more than 10% \\'\\n                          \\'for some traits. This\\\\n\\'\\n                          \\'research marks a significant step forward in \\'\\n                          \\'understanding the capabilities of\\\\n\\'\\n                          \\'LLMs to express personality traits.\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20230504\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2305.02547\\',\\n            \\'title\\': \\'PersonaLLM: Investigating the Ability of Large Language \\'\\n                     \\'Models to Express Big Five Personality Traits\\',\\n            \\'updated\\': \\'20231116\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2106.13973\\',\\n \\'_score\\': 0.000539980421308428,\\n \\'fields\\': {\\'authors\\': \\'Priyam Basu, Tiasa Singha Roy, Rakshit Naidu, Zumrut \\'\\n                       \\'Muftuoglu, Sahib Singh, Fatemehsadat Mireshghallah\\',\\n            \\'categories\\': \\'cs.CL, cs.CR, cs.LG\\',\\n            \\'chunk_text\\': \\'Natural Language Processing (NLP) techniques can be \\'\\n                          \\'applied to help with the\\\\n\\'\\n                          \\'diagnosis of medical conditions such as depression, \\'\\n                          \\'using a collection of a\\\\n\\'\\n                          \"person\\'s utterances. Depression is a serious \"\\n                          \\'medical illness that can have\\\\n\\'\\n                          \\'adverse effects on how one feels, thinks, and acts, \\'\\n                          \\'which can lead to emotional\\\\n\\'\\n                          \\'and physical problems. Due to the sensitive nature \\'\\n                          \\'of such data, privacy\\\\n\\'\\n                          \\'measures need to be taken for handling and training \\'\\n                          \\'models with such data. In\\\\n\\'\\n                          \\'this work, we study the effects that the \\'\\n                          \\'application of Differential Privacy\\\\n\\'\\n                          \\'(DP) has, in both a centralized and a Federated \\'\\n                          \\'Learning (FL) setup, on\\\\n\\'\\n                          \\'training contextualized language models (BERT, \\'\\n                          \\'ALBERT, RoBERTa and DistilBERT).\\\\n\\'\\n                          \\'We offer insights on how to privately train NLP \\'\\n                          \\'models and what architectures\\\\n\\'\\n                          \\'and setups provide more desirable privacy utility \\'\\n                          \\'trade-offs. We envisage this\\\\n\\'\\n                          \\'work to be used in future healthcare and mental \\'\\n                          \\'health studies to keep medical\\\\n\\'\\n                          \\'history private. Therefore, we provide an \\'\\n                          \\'open-source implementation of this\\\\n\\'\\n                          \\'work.\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20210626\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2106.13973\\',\\n            \\'title\\': \\'Benchmarking Differential Privacy and Federated Learning \\'\\n                     \\'for BERT Models\\',\\n            \\'updated\\': \\'20220616\\'}}}]'}]}]\n",
      "RESPONSE:\n",
      "Message(id='msg_01CG8J5nubVKrXD8kSuf733m', content=[TextBlock(citations=None, text='After thorough searching, I didn\\'t find any papers about personal information specifically authored by Loubna Ben Allal in the previous search results. However, I did find that she has published papers, including one called \"SantaCoder: don\\'t reach for the stars!\" with many co-authors.\\n\\nTo ensure completeness, I\\'ll do one more search:', type='text'), ToolUseBlock(id='toolu_01TpD6ehSv2gqt8b56W78QMs', input={'query': 'Loubna Ben Allal research papers on personal information privacy'}, name='web_search_engine', type='tool_use')], model='claude-3-5-haiku-20241022', role='assistant', stop_reason='tool_use', stop_sequence=None, type='message', usage=Usage(cache_creation_input_tokens=0, cache_read_input_tokens=0, input_tokens=8445, output_tokens=147, server_tool_use=None, service_tier='standard'))\n",
      "TOOL CALLS:\n",
      "[TextBlock(citations=None, text='After thorough searching, I didn\\'t find any papers about personal information specifically authored by Loubna Ben Allal in the previous search results. However, I did find that she has published papers, including one called \"SantaCoder: don\\'t reach for the stars!\" with many co-authors.\\n\\nTo ensure completeness, I\\'ll do one more search:', type='text'), ToolUseBlock(id='toolu_01TpD6ehSv2gqt8b56W78QMs', input={'query': 'Loubna Ben Allal research papers on personal information privacy'}, name='web_search_engine', type='tool_use')]\n",
      "Not a tool_use content block: TextBlock(citations=None, text='After thorough searching, I didn\\'t find any papers about personal information specifically authored by Loubna Ben Allal in the previous search results. However, I did find that she has published papers, including one called \"SantaCoder: don\\'t reach for the stars!\" with many co-authors.\\n\\nTo ensure completeness, I\\'ll do one more search:', type='text')\n",
      "Tool name: web_search_engine | Parameters: {'query': 'Loubna Ben Allal research papers on personal information privacy'}\n",
      "==================================================\n",
      "calling step 4 with:\n",
      "[{'role': 'user', 'content': 'What papers are about personal information?'}, {'role': 'assistant', 'content': [TextBlock(citations=None, text=\"I'll search for relevant academic papers about personal information using the Pinecone vector search.\", type='text'), ToolUseBlock(id='toolu_01QtZPpAzu66MD5cTB7LKAiN', input={'query': 'personal information privacy research papers'}, name='pinecone_vector_search', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01QtZPpAzu66MD5cTB7LKAiN', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2202.00443\\',\\n \\'_score\\': 0.25552940368652344,\\n \\'fields\\': {\\'authors\\': \\'Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi \\'\\n                       \\'Papadopoulou, David Sánchez, Montserrat Batet\\',\\n            \\'categories\\': \\'cs.CL, cs.AI\\',\\n            \\'chunk_text\\': \\'We present a novel benchmark and associated \\'\\n                          \\'evaluation metrics for assessing\\\\n\\'\\n                          \\'the performance of text anonymization methods. Text \\'\\n                          \\'anonymization, defined as\\\\n\\'\\n                          \\'the task of editing a text document to prevent the \\'\\n                          \\'disclosure of personal\\\\n\\'\\n                          \\'information, currently suffers from a shortage of \\'\\n                          \\'privacy-oriented annotated\\\\n\\'\\n                          \\'text resources, making it difficult to properly \\'\\n                          \\'evaluate the level of privacy\\\\n\\'\\n                          \\'protection offered by various anonymization \\'\\n                          \\'methods. This paper presents TAB\\\\n\\'\\n                          \\'(Text Anonymization Benchmark), a new, open-source \\'\\n                          \\'annotated corpus developed\\\\n\\'\\n                          \\'to address this shortage. The corpus comprises \\'\\n                          \\'1,268 English-language court\\\\n\\'\\n                          \\'cases from the European Court of Human Rights \\'\\n                          \\'(ECHR) enriched with\\\\n\\'\\n                          \\'comprehensive annotations about the personal \\'\\n                          \\'information appearing in each\\\\n\\'\\n                          \\'document, including their semantic category, \\'\\n                          \\'identifier type, confidential\\\\n\\'\\n                          \\'attributes, and co-reference relations. Compared to \\'\\n                          \\'previous work, the TAB\\\\n\\'\\n                          \\'corpus is designed to go beyond traditional \\'\\n                          \\'de-identification (which is limited\\\\n\\'\\n                          \\'to the detection of predefined semantic \\'\\n                          \\'categories), and explicitly marks which\\\\n\\'\\n                          \\'text spans ought to be masked in order to conceal \\'\\n                          \\'the identity of the person to\\\\n\\'\\n                          \\'be protected. Along with presenting the corpus and \\'\\n                          \\'its annotation layers, we\\\\n\\'\\n                          \\'also propose a set of evaluation metrics that are \\'\\n                          \\'specifically tailored towards\\\\n\\'\\n                          \\'measuring the performance of text anonymization, \\'\\n                          \\'both in terms of privacy\\\\n\\'\\n                          \\'protection and utility preservation. We illustrate \\'\\n                          \\'the use of the benchmark and\\\\n\\'\\n                          \\'the proposed metrics by assessing the empirical \\'\\n                          \\'performance of several baseline\\\\n\\'\\n                          \\'text anonymization models. The full corpus along \\'\\n                          \\'with its privacy-oriented\\\\n\\'\\n                          \\'annotation guidelines, evaluation scripts and \\'\\n                          \\'baseline models are available on:\\\\n\\'\\n                          \\'https://github.com/NorskRegnesentral/text-anonymisation-benchmark\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20220125\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2202.00443\\',\\n            \\'title\\': \\'The Text Anonymization Benchmark (TAB): A Dedicated \\'\\n                     \\'Corpus and Evaluation Framework for Text Anonymization\\',\\n            \\'updated\\': \\'20220701\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2205.12628\\',\\n \\'_score\\': 0.12137297540903091,\\n \\'fields\\': {\\'authors\\': \\'Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang\\',\\n            \\'categories\\': \\'cs.CL, cs.AI, cs.CR\\',\\n            \\'chunk_text\\': \\'Are Large Pre-Trained Language Models Leaking Your \\'\\n                          \\'Personal Information? In\\\\n\\'\\n                          \\'this paper, we analyze whether Pre-Trained Language \\'\\n                          \\'Models (PLMs) are prone to\\\\n\\'\\n                          \\'leaking personal information. Specifically, we \\'\\n                          \\'query PLMs for email addresses\\\\n\\'\\n                          \\'with contexts of the email address or prompts \\'\\n                          \"containing the owner\\'s name. We\\\\n\"\\n                          \\'find that PLMs do leak personal information due to \\'\\n                          \\'memorization. However, since\\\\n\\'\\n                          \\'the models are weak at association, the risk of \\'\\n                          \\'specific personal information\\\\n\\'\\n                          \\'being extracted by attackers is low. We hope this \\'\\n                          \\'work could help the community\\\\n\\'\\n                          \\'to better understand the privacy risk of PLMs and \\'\\n                          \\'bring new insights to make\\\\n\\'\\n                          \\'PLMs safe.\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20220525\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2205.12628\\',\\n            \\'title\\': \\'Are Large Pre-Trained Language Models Leaking Your \\'\\n                     \\'Personal Information?\\',\\n            \\'updated\\': \\'20221020\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2302.03262\\',\\n \\'_score\\': 0.10176784545183182,\\n \\'fields\\': {\\'authors\\': \\'Tomoya Matsumoto, Takayuki Miura, Naoto Yanai\\',\\n            \\'categories\\': \\'cs.CR, cs.LG\\',\\n            \\'chunk_text\\': \\'Diffusion models have attracted attention in recent \\'\\n                          \\'years as innovative\\\\n\\'\\n                          \\'generative models. In this paper, we investigate \\'\\n                          \\'whether a diffusion model is\\\\n\\'\\n                          \\'resistant to a membership inference attack, which \\'\\n                          \\'evaluates the privacy leakage\\\\n\\'\\n                          \\'of a machine learning model. We primarily discuss \\'\\n                          \\'the diffusion model from the\\\\n\\'\\n                          \\'standpoints of comparison with a generative \\'\\n                          \\'adversarial network (GAN) as\\\\n\\'\\n                          \\'conventional models and hyperparameters unique to \\'\\n                          \\'the diffusion model, i.e.,\\\\n\\'\\n                          \\'time steps, sampling steps, and sampling variances. \\'\\n                          \\'We conduct extensive\\\\n\\'\\n                          \\'experiments with DDIM as a diffusion model and \\'\\n                          \\'DCGAN as a GAN on the CelebA and\\\\n\\'\\n                          \\'CIFAR-10 datasets in both white-box and black-box \\'\\n                          \\'settings and then confirm if\\\\n\\'\\n                          \\'the diffusion model is comparably resistant to a \\'\\n                          \\'membership inference attack as\\\\n\\'\\n                          \\'GAN. Next, we demonstrate that the impact of time \\'\\n                          \\'steps is significant and\\\\n\\'\\n                          \\'intermediate steps in a noise schedule are the most \\'\\n                          \\'vulnerable to the attack.\\\\n\\'\\n                          \\'We also found two key insights through further \\'\\n                          \\'analysis. First, we identify\\\\n\\'\\n                          \\'that DDIM is vulnerable to the attack for small \\'\\n                          \\'sample sizes instead of\\\\n\\'\\n                          \\'achieving a lower FID. Second, sampling steps in \\'\\n                          \\'hyperparameters are important\\\\n\\'\\n                          \\'for resistance to the attack, whereas the impact of \\'\\n                          \\'sampling variances is quite\\\\n\\'\\n                          \\'limited.\\',\\n            \\'primary_category\\': \\'cs.CR\\',\\n            \\'published\\': \\'20230207\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2302.03262\\',\\n            \\'title\\': \\'Membership Inference Attacks against Diffusion Models\\',\\n            \\'updated\\': \\'20230322\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2209.02299\\',\\n \\'_score\\': 0.08021324872970581,\\n \\'fields\\': {\\'authors\\': \\'Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, \\'\\n                       \\'Alan Wee-Chung Liew, Hongzhi Yin, Quoc Viet Hung \\'\\n                       \\'Nguyen\\',\\n            \\'categories\\': \\'cs.LG, cs.AI\\',\\n            \\'chunk_text\\': \\'Today, computer systems hold large amounts of \\'\\n                          \\'personal data. Yet while such\\\\n\\'\\n                          \\'an abundance of data allows breakthroughs in \\'\\n                          \\'artificial intelligence, and\\\\n\\'\\n                          \\'especially machine learning (ML), its existence can \\'\\n                          \\'be a threat to user\\\\n\\'\\n                          \\'privacy, and it can weaken the bonds of trust \\'\\n                          \\'between humans and AI. Recent\\\\n\\'\\n                          \\'regulations now require that, on request, private \\'\\n                          \\'information about a user must\\\\n\\'\\n                          \\'be removed from both computer systems and from ML \\'\\n                          \\'models, i.e. ``the right to\\\\n\\'\\n                          \"be forgotten\\'\\'). While removing data from back-end \"\\n                          \\'databases should be\\\\n\\'\\n                          \\'straightforward, it is not sufficient in the AI \\'\\n                          \\'context as ML models often\\\\n\\'\\n                          \"`remember\\' the old data. Contemporary adversarial \"\\n                          \\'attacks on trained models\\\\n\\'\\n                          \\'have proven that we can learn whether an instance \\'\\n                          \\'or an attribute belonged to\\\\n\\'\\n                          \\'the training data. This phenomenon calls for a new \\'\\n                          \\'paradigm, namely machine\\\\n\\'\\n                          \\'unlearning, to make ML models forget about \\'\\n                          \\'particular data. It turns out that\\\\n\\'\\n                          \\'recent works on machine unlearning have not been \\'\\n                          \\'able to completely solve the\\\\n\\'\\n                          \\'problem due to the lack of common frameworks and \\'\\n                          \\'resources. Therefore, this\\\\n\\'\\n                          \\'paper aspires to present a comprehensive \\'\\n                          \"examination of machine unlearning\\'s\\\\n\"\\n                          \\'concepts, scenarios, methods, and applications. \\'\\n                          \\'Specifically, as a category\\\\n\\'\\n                          \\'collection of cutting-edge studies, the intention \\'\\n                          \\'behind this article is to\\\\n\\'\\n                          \\'serve as a comprehensive resource for researchers \\'\\n                          \\'and practitioners seeking an\\\\n\\'\\n                          \\'introduction to machine unlearning and its \\'\\n                          \\'formulations, design criteria,\\\\n\\'\\n                          \\'removal requests, algorithms, and applications. In \\'\\n                          \\'addition, we aim to\\\\n\\'\\n                          \\'highlight the key findings, current trends, and new \\'\\n                          \\'research areas that have\\\\n\\'\\n                          \\'not yet featured the use of machine unlearning but \\'\\n                          \\'could benefit greatly from\\\\n\\'\\n                          \\'it. We hope this survey serves as a valuable \\'\\n                          \\'resource for ML researchers and\\\\n\\'\\n                          \\'those seeking to innovate privacy technologies. Our \\'\\n                          \\'resources are publicly\\\\n\\'\\n                          \\'available at \\'\\n                          \\'https://github.com/tamlhp/awesome-machine-unlearning.\\',\\n            \\'primary_category\\': \\'cs.LG\\',\\n            \\'published\\': \\'20220906\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2209.02299\\',\\n            \\'title\\': \\'A Survey of Machine Unlearning\\',\\n            \\'updated\\': \\'20221021\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2301.09956\\',\\n \\'_score\\': 0.06313489377498627,\\n \\'fields\\': {\\'authors\\': \\'Hailong Hu, Jun Pang\\',\\n            \\'categories\\': \\'cs.CR, cs.LG\\',\\n            \\'chunk_text\\': \\'Recent years have witnessed the tremendous success \\'\\n                          \\'of diffusion models in\\\\n\\'\\n                          \\'data synthesis. However, when diffusion models are \\'\\n                          \\'applied to sensitive data,\\\\n\\'\\n                          \\'they also give rise to severe privacy concerns. In \\'\\n                          \\'this paper, we\\\\n\\'\\n                          \\'systematically present the first study about \\'\\n                          \\'membership inference attacks\\\\n\\'\\n                          \\'against diffusion models, which aims to infer \\'\\n                          \\'whether a sample was used to\\\\n\\'\\n                          \\'train the model. Two attack methods are proposed, \\'\\n                          \\'namely loss-based and\\\\n\\'\\n                          \\'likelihood-based attacks. Our attack methods are \\'\\n                          \\'evaluated on several\\\\n\\'\\n                          \\'state-of-the-art diffusion models, over different \\'\\n                          \\'datasets in relation to\\\\n\\'\\n                          \\'privacy-sensitive data. Extensive experimental \\'\\n                          \\'evaluations show that our\\\\n\\'\\n                          \\'attacks can achieve remarkable performance. \\'\\n                          \\'Furthermore, we exhaustively\\\\n\\'\\n                          \\'investigate various factors which can affect attack \\'\\n                          \\'performance. Finally, we\\\\n\\'\\n                          \\'also evaluate the performance of our attack methods \\'\\n                          \\'on diffusion models trained\\\\n\\'\\n                          \\'with differential privacy.\\',\\n            \\'primary_category\\': \\'cs.CR\\',\\n            \\'published\\': \\'20230124\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2301.09956\\',\\n            \\'title\\': \\'Membership Inference of Diffusion Models\\',\\n            \\'updated\\': \\'20230124\\'}}}]'}]}, {'role': 'assistant', 'content': 'Based on the search results, I can highlight several interesting papers about personal information:\\n\\n1. \"The Text Anonymization Benchmark (TAB)\" (2022)\\n- This paper presents a benchmark for text anonymization\\n- Focuses on protecting personal information in text documents\\n- Developed using 1,268 court cases from the European Court of Human Rights\\n- Aims to improve methods for concealing personal identities in documents\\n\\n2. \"Are Large Pre-Trained Language Models Leaking Your Personal Information?\" (2022)\\n- Investigates whether large language models can inadvertently leak personal information\\n- Explores potential privacy risks in AI language models\\n- Found that while models can memorize personal information, the risk of extraction is relatively low\\n\\n3. \"A Survey of Machine Unlearning\" (2022)\\n- Examines the concept of \"the right to be forgotten\" in machine learning\\n- Discusses how personal data can be removed from computer systems and AI models\\n- Highlights the challenges of completely erasing personal information from trained models\\n\\n4. \"Membership Inference of Diffusion Models\" (2023)\\n- Investigates privacy concerns in AI models, specifically diffusion models\\n- Explores techniques to determine if a specific piece of data was used in training a model\\n- Focuses on protecting sensitive personal information in machine learning contexts\\n\\nThese papers demonstrate the growing academic interest in protecting personal information, particularly in the context of artificial intelligence, machine learning, and data privacy. They cover various aspects such as text anonymization, model privacy, and methods to prevent unintended personal data exposure.\\n\\nWould you like me to elaborate on any of these papers or search for more specific information about personal information research?'}, {'role': 'user', 'content': 'Are any of these written by Loubna Ben Allal?'}, {'role': 'assistant', 'content': [TextBlock(citations=None, text=\"I'll search to check if Loubna Ben Allal is an author on any of these papers.\", type='text'), ToolUseBlock(id='toolu_01SifprLfnGMkZtYAEeKvQxH', input={'query': 'Loubna Ben Allal author personal information papers'}, name='web_search_engine', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01SifprLfnGMkZtYAEeKvQxH', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'LOUBNA BEN ALLAL - Google Scholar\\', \\'href\\': \\'https://scholar.google.com/citations?user=reU1i-sAAAAJ\\', \\'body\\': \\'\\\\u202aHugging Face\\\\u202c - \\\\u202a\\\\u202aCited by 4,518\\\\u202c\\\\u202c - \\\\u202aMachine Learning\\\\u202c\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'Loubna Ben Allal - Home - ACM Digital Library\\', \\'href\\': \\'https://dl.acm.org/profile/99661625996\\', \\'body\\': \"Colleague Collaboration Author\\'s Latest Publications research-article Scaling laws and compute-optimal training beyond fixed training durations Alexander Hägele, Elie Bakouch, Atli Kosson, + 3\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \"[2301.03988] SantaCoder: don\\'t reach for the stars! - arXiv.org\", \\'href\\': \\'https://arxiv.org/abs/2301.03988\\', \\'body\\': \"View a PDF of the paper titled SantaCoder: don\\'t reach for the stars!, by Loubna Ben Allal and 40 other authors\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'loubnabnl (Loubna Ben Allal) - Hugging Face\\', \\'href\\': \\'https://huggingface.co/loubnabnl\\', \\'body\\': \\'Papers11 arxiv:2506.05209 arxiv:2504.05299 arxiv:2502.02737 arxiv:2406.17557 Expand 11 papers spaces6\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'Loubna Ben allal | Papers With Code\\', \\'href\\': \\'https://paperswithcode.com/search?q=author:Loubna+Ben+allal\\', \\'body\\': \\'Papers by Loubna Ben allal with links to code and results.\\'}}]'}]}, {'role': 'assistant', 'content': [TextBlock(citations=None, text=\"I'll do another search to find her specific papers about personal information:\", type='text'), ToolUseBlock(id='toolu_017MGyC5QaPYFGgSRWHe2dmM', input={'query': 'Loubna Ben Allal personal information privacy research'}, name='pinecone_vector_search', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_017MGyC5QaPYFGgSRWHe2dmM', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2202.00443\\',\\n \\'_score\\': 0.0022694687359035015,\\n \\'fields\\': {\\'authors\\': \\'Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi \\'\\n                       \\'Papadopoulou, David Sánchez, Montserrat Batet\\',\\n            \\'categories\\': \\'cs.CL, cs.AI\\',\\n            \\'chunk_text\\': \\'We present a novel benchmark and associated \\'\\n                          \\'evaluation metrics for assessing\\\\n\\'\\n                          \\'the performance of text anonymization methods. Text \\'\\n                          \\'anonymization, defined as\\\\n\\'\\n                          \\'the task of editing a text document to prevent the \\'\\n                          \\'disclosure of personal\\\\n\\'\\n                          \\'information, currently suffers from a shortage of \\'\\n                          \\'privacy-oriented annotated\\\\n\\'\\n                          \\'text resources, making it difficult to properly \\'\\n                          \\'evaluate the level of privacy\\\\n\\'\\n                          \\'protection offered by various anonymization \\'\\n                          \\'methods. This paper presents TAB\\\\n\\'\\n                          \\'(Text Anonymization Benchmark), a new, open-source \\'\\n                          \\'annotated corpus developed\\\\n\\'\\n                          \\'to address this shortage. The corpus comprises \\'\\n                          \\'1,268 English-language court\\\\n\\'\\n                          \\'cases from the European Court of Human Rights \\'\\n                          \\'(ECHR) enriched with\\\\n\\'\\n                          \\'comprehensive annotations about the personal \\'\\n                          \\'information appearing in each\\\\n\\'\\n                          \\'document, including their semantic category, \\'\\n                          \\'identifier type, confidential\\\\n\\'\\n                          \\'attributes, and co-reference relations. Compared to \\'\\n                          \\'previous work, the TAB\\\\n\\'\\n                          \\'corpus is designed to go beyond traditional \\'\\n                          \\'de-identification (which is limited\\\\n\\'\\n                          \\'to the detection of predefined semantic \\'\\n                          \\'categories), and explicitly marks which\\\\n\\'\\n                          \\'text spans ought to be masked in order to conceal \\'\\n                          \\'the identity of the person to\\\\n\\'\\n                          \\'be protected. Along with presenting the corpus and \\'\\n                          \\'its annotation layers, we\\\\n\\'\\n                          \\'also propose a set of evaluation metrics that are \\'\\n                          \\'specifically tailored towards\\\\n\\'\\n                          \\'measuring the performance of text anonymization, \\'\\n                          \\'both in terms of privacy\\\\n\\'\\n                          \\'protection and utility preservation. We illustrate \\'\\n                          \\'the use of the benchmark and\\\\n\\'\\n                          \\'the proposed metrics by assessing the empirical \\'\\n                          \\'performance of several baseline\\\\n\\'\\n                          \\'text anonymization models. The full corpus along \\'\\n                          \\'with its privacy-oriented\\\\n\\'\\n                          \\'annotation guidelines, evaluation scripts and \\'\\n                          \\'baseline models are available on:\\\\n\\'\\n                          \\'https://github.com/NorskRegnesentral/text-anonymisation-benchmark\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20220125\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2202.00443\\',\\n            \\'title\\': \\'The Text Anonymization Benchmark (TAB): A Dedicated \\'\\n                     \\'Corpus and Evaluation Framework for Text Anonymization\\',\\n            \\'updated\\': \\'20220701\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2209.02299\\',\\n \\'_score\\': 0.001674399245530367,\\n \\'fields\\': {\\'authors\\': \\'Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, \\'\\n                       \\'Alan Wee-Chung Liew, Hongzhi Yin, Quoc Viet Hung \\'\\n                       \\'Nguyen\\',\\n            \\'categories\\': \\'cs.LG, cs.AI\\',\\n            \\'chunk_text\\': \\'Today, computer systems hold large amounts of \\'\\n                          \\'personal data. Yet while such\\\\n\\'\\n                          \\'an abundance of data allows breakthroughs in \\'\\n                          \\'artificial intelligence, and\\\\n\\'\\n                          \\'especially machine learning (ML), its existence can \\'\\n                          \\'be a threat to user\\\\n\\'\\n                          \\'privacy, and it can weaken the bonds of trust \\'\\n                          \\'between humans and AI. Recent\\\\n\\'\\n                          \\'regulations now require that, on request, private \\'\\n                          \\'information about a user must\\\\n\\'\\n                          \\'be removed from both computer systems and from ML \\'\\n                          \\'models, i.e. ``the right to\\\\n\\'\\n                          \"be forgotten\\'\\'). While removing data from back-end \"\\n                          \\'databases should be\\\\n\\'\\n                          \\'straightforward, it is not sufficient in the AI \\'\\n                          \\'context as ML models often\\\\n\\'\\n                          \"`remember\\' the old data. Contemporary adversarial \"\\n                          \\'attacks on trained models\\\\n\\'\\n                          \\'have proven that we can learn whether an instance \\'\\n                          \\'or an attribute belonged to\\\\n\\'\\n                          \\'the training data. This phenomenon calls for a new \\'\\n                          \\'paradigm, namely machine\\\\n\\'\\n                          \\'unlearning, to make ML models forget about \\'\\n                          \\'particular data. It turns out that\\\\n\\'\\n                          \\'recent works on machine unlearning have not been \\'\\n                          \\'able to completely solve the\\\\n\\'\\n                          \\'problem due to the lack of common frameworks and \\'\\n                          \\'resources. Therefore, this\\\\n\\'\\n                          \\'paper aspires to present a comprehensive \\'\\n                          \"examination of machine unlearning\\'s\\\\n\"\\n                          \\'concepts, scenarios, methods, and applications. \\'\\n                          \\'Specifically, as a category\\\\n\\'\\n                          \\'collection of cutting-edge studies, the intention \\'\\n                          \\'behind this article is to\\\\n\\'\\n                          \\'serve as a comprehensive resource for researchers \\'\\n                          \\'and practitioners seeking an\\\\n\\'\\n                          \\'introduction to machine unlearning and its \\'\\n                          \\'formulations, design criteria,\\\\n\\'\\n                          \\'removal requests, algorithms, and applications. In \\'\\n                          \\'addition, we aim to\\\\n\\'\\n                          \\'highlight the key findings, current trends, and new \\'\\n                          \\'research areas that have\\\\n\\'\\n                          \\'not yet featured the use of machine unlearning but \\'\\n                          \\'could benefit greatly from\\\\n\\'\\n                          \\'it. We hope this survey serves as a valuable \\'\\n                          \\'resource for ML researchers and\\\\n\\'\\n                          \\'those seeking to innovate privacy technologies. Our \\'\\n                          \\'resources are publicly\\\\n\\'\\n                          \\'available at \\'\\n                          \\'https://github.com/tamlhp/awesome-machine-unlearning.\\',\\n            \\'primary_category\\': \\'cs.LG\\',\\n            \\'published\\': \\'20220906\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2209.02299\\',\\n            \\'title\\': \\'A Survey of Machine Unlearning\\',\\n            \\'updated\\': \\'20221021\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2202.05520\\',\\n \\'_score\\': 0.0010733063099905849,\\n \\'fields\\': {\\'authors\\': \\'Hannah Brown, Katherine Lee, Fatemehsadat \\'\\n                       \\'Mireshghallah, Reza Shokri, Florian Tramèr\\',\\n            \\'categories\\': \\'stat.ML, cs.CL, cs.LG\\',\\n            \\'chunk_text\\': \\'Natural language reflects our private lives and \\'\\n                          \\'identities, making its\\\\n\\'\\n                          \\'privacy concerns as broad as those of real life. \\'\\n                          \\'Language models lack the\\\\n\\'\\n                          \\'ability to understand the context and sensitivity \\'\\n                          \\'of text, and tend to memorize\\\\n\\'\\n                          \\'phrases present in their training sets. An \\'\\n                          \\'adversary can exploit this tendency\\\\n\\'\\n                          \\'to extract training data. Depending on the nature \\'\\n                          \\'of the content and the\\\\n\\'\\n                          \\'context in which this data was collected, this \\'\\n                          \\'could violate expectations of\\\\n\\'\\n                          \\'privacy. Thus there is a growing interest in \\'\\n                          \\'techniques for training language\\\\n\\'\\n                          \\'models that preserve privacy. In this paper, we \\'\\n                          \\'discuss the mismatch between\\\\n\\'\\n                          \\'the narrow assumptions made by popular data \\'\\n                          \\'protection techniques (data\\\\n\\'\\n                          \\'sanitization and differential privacy), and the \\'\\n                          \\'broadness of natural language\\\\n\\'\\n                          \\'and of privacy as a social norm. We argue that \\'\\n                          \\'existing protection methods\\\\n\\'\\n                          \\'cannot guarantee a generic and meaningful notion of \\'\\n                          \\'privacy for language\\\\n\\'\\n                          \\'models. We conclude that language models should be \\'\\n                          \\'trained on text data which\\\\n\\'\\n                          \\'was explicitly produced for public use.\\',\\n            \\'primary_category\\': \\'stat.ML\\',\\n            \\'published\\': \\'20220211\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2202.05520\\',\\n            \\'title\\': \\'What Does it Mean for a Language Model to Preserve \\'\\n                     \\'Privacy?\\',\\n            \\'updated\\': \\'20220214\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2305.02547\\',\\n \\'_score\\': 0.0008559006964787841,\\n \\'fields\\': {\\'authors\\': \\'Hang Jiang, Xiajie Zhang, Xubo Cao, Jad Kabbara\\',\\n            \\'categories\\': \\'cs.CL, cs.AI, cs.HC\\',\\n            \\'chunk_text\\': \\'Despite the many use cases for large language \\'\\n                          \\'models (LLMs) in creating\\\\n\\'\\n                          \\'personalized chatbots, there has been limited \\'\\n                          \\'research on evaluating the extent\\\\n\\'\\n                          \\'to which the behaviors of personalized LLMs \\'\\n                          \\'accurately and consistently reflect\\\\n\\'\\n                          \\'specific personality traits. We consider studying \\'\\n                          \\'the behavior of LLM-based\\\\n\\'\\n                          \\'agents, referred to as LLM personas, and present a \\'\\n                          \\'case study with ChatGPT and\\\\n\\'\\n                          \\'GPT-4. The study investigates whether LLMs can \\'\\n                          \\'generate content that aligns\\\\n\\'\\n                          \\'with their assigned personality profiles. To this \\'\\n                          \\'end, we create distinct LLM\\\\n\\'\\n                          \\'personas based on the Big Five personality model, \\'\\n                          \\'have them complete the\\\\n\\'\\n                          \\'44-item Big Five Inventory (BFI) personality test \\'\\n                          \\'and a story writing task, and\\\\n\\'\\n                          \\'then assess their essays with automatic and human \\'\\n                          \\'evaluations. Results show\\\\n\\'\\n                          \"that LLM personas\\' self-reported BFI scores are \"\\n                          \\'consistent with their\\\\n\\'\\n                          \\'designated personality types, with large effect \\'\\n                          \\'sizes observed across five\\\\n\\'\\n                          \\'traits. Additionally, there are significant \\'\\n                          \\'correlations between the assigned\\\\n\\'\\n                          \\'personality types and certain psycholinguistic \\'\\n                          \\'features of their writings, as\\\\n\\'\\n                          \\'measured by the Linguistic Inquiry and Word Count \\'\\n                          \\'(LIWC) tool. Interestingly,\\\\n\\'\\n                          \\'human evaluators perceive the stories as less \\'\\n                          \\'personal when told that the\\\\n\\'\\n                          \\'stories are authored by AI. However, their \\'\\n                          \\'judgments on other aspects of the\\\\n\\'\\n                          \\'writing such as readability, cohesiveness, \\'\\n                          \\'redundancy, likeability, and\\\\n\\'\\n                          \\'believability remain largely unaffected. Notably, \\'\\n                          \\'when evaluators were informed\\\\n\\'\\n                          \\'about the AI authorship, their accuracy in \\'\\n                          \\'identifying the intended personality\\\\n\\'\\n                          \\'traits from the stories decreased by more than 10% \\'\\n                          \\'for some traits. This\\\\n\\'\\n                          \\'research marks a significant step forward in \\'\\n                          \\'understanding the capabilities of\\\\n\\'\\n                          \\'LLMs to express personality traits.\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20230504\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2305.02547\\',\\n            \\'title\\': \\'PersonaLLM: Investigating the Ability of Large Language \\'\\n                     \\'Models to Express Big Five Personality Traits\\',\\n            \\'updated\\': \\'20231116\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2106.13973\\',\\n \\'_score\\': 0.000539980421308428,\\n \\'fields\\': {\\'authors\\': \\'Priyam Basu, Tiasa Singha Roy, Rakshit Naidu, Zumrut \\'\\n                       \\'Muftuoglu, Sahib Singh, Fatemehsadat Mireshghallah\\',\\n            \\'categories\\': \\'cs.CL, cs.CR, cs.LG\\',\\n            \\'chunk_text\\': \\'Natural Language Processing (NLP) techniques can be \\'\\n                          \\'applied to help with the\\\\n\\'\\n                          \\'diagnosis of medical conditions such as depression, \\'\\n                          \\'using a collection of a\\\\n\\'\\n                          \"person\\'s utterances. Depression is a serious \"\\n                          \\'medical illness that can have\\\\n\\'\\n                          \\'adverse effects on how one feels, thinks, and acts, \\'\\n                          \\'which can lead to emotional\\\\n\\'\\n                          \\'and physical problems. Due to the sensitive nature \\'\\n                          \\'of such data, privacy\\\\n\\'\\n                          \\'measures need to be taken for handling and training \\'\\n                          \\'models with such data. In\\\\n\\'\\n                          \\'this work, we study the effects that the \\'\\n                          \\'application of Differential Privacy\\\\n\\'\\n                          \\'(DP) has, in both a centralized and a Federated \\'\\n                          \\'Learning (FL) setup, on\\\\n\\'\\n                          \\'training contextualized language models (BERT, \\'\\n                          \\'ALBERT, RoBERTa and DistilBERT).\\\\n\\'\\n                          \\'We offer insights on how to privately train NLP \\'\\n                          \\'models and what architectures\\\\n\\'\\n                          \\'and setups provide more desirable privacy utility \\'\\n                          \\'trade-offs. We envisage this\\\\n\\'\\n                          \\'work to be used in future healthcare and mental \\'\\n                          \\'health studies to keep medical\\\\n\\'\\n                          \\'history private. Therefore, we provide an \\'\\n                          \\'open-source implementation of this\\\\n\\'\\n                          \\'work.\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20210626\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2106.13973\\',\\n            \\'title\\': \\'Benchmarking Differential Privacy and Federated Learning \\'\\n                     \\'for BERT Models\\',\\n            \\'updated\\': \\'20220616\\'}}}]'}]}, {'role': 'assistant', 'content': [TextBlock(citations=None, text='After thorough searching, I didn\\'t find any papers about personal information specifically authored by Loubna Ben Allal in the previous search results. However, I did find that she has published papers, including one called \"SantaCoder: don\\'t reach for the stars!\" with many co-authors.\\n\\nTo ensure completeness, I\\'ll do one more search:', type='text'), ToolUseBlock(id='toolu_01TpD6ehSv2gqt8b56W78QMs', input={'query': 'Loubna Ben Allal research papers on personal information privacy'}, name='web_search_engine', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01TpD6ehSv2gqt8b56W78QMs', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'LOUBNA BEN ALLAL - Google Scholar\\', \\'href\\': \\'https://scholar.google.com/citations?user=reU1i-sAAAAJ\\', \\'body\\': \\'\\\\u202aHugging Face\\\\u202c - \\\\u202a\\\\u202aCited by 4,518\\\\u202c\\\\u202c - \\\\u202aMachine Learning\\\\u202c\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'arXiv:2301.03988v2 [cs.SE] 24 Feb 2023\\', \\'href\\': \\'https://arxiv.org/pdf/2301.03988\\', \\'body\\': \\'The BigCode project is an open-scientific collaboration working on the responsi-ble development of large language models for code.1 This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experi-ments conducted to de-risk the model architecture, and the experiments ...\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'(PDF) StarCoder: may the source be with you! - ResearchGate\\', \\'href\\': \\'https://www.researchgate.net/publication/370656499_StarCoder_may_the_source_be_with_you\\', \\'body\\': \"Concerns about personal information led Italy to temporarily ban ChatGPT and launch an on- going investigation into OpenAI\\' s compliance with the E.U. \\'s General Data Protection Regula-\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'GitHub Pages - Loubna Ben Allal\\', \\'href\\': \\'https://loubnabnl.github.io/\\', \\'body\\': \"Welcome to my personal page! 🌸 I\\'m Loubna, a Research Engineer at Hugging Face. I lead efforts on training small Language Models (SmolLM & SmolLM2) and building pre-training datasets like Cosmopedia and FineWeb-Edu.\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'Loubna Ben Allal To cite this version - inria.hal.science\\', \\'href\\': \\'https://inria.hal.science/hal-02947341/document\\', \\'body\\': \"The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L\\'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d\\'enseignement et de recherche ...\"}}]'}]}]\n",
      "RESPONSE:\n",
      "Message(id='msg_01QSxRrVDmKt8bS4urqXM5KU', content=[TextBlock(citations=None, text=\"Interestingly, I found a connection to personal information in the BigCode project paper, which mentions work on Personally Identifiable Information (PII) redaction. However, while Loubna Ben Allal is associated with this project, none of the papers I found are exclusively written by her about personal information.\\n\\nTo summarize:\\n- Loubna Ben Allal is a Research Engineer at Hugging Face\\n- She has been involved in projects related to language models\\n- She's part of the BigCode project, which includes work on PII (Personally Identifiable Information) redaction\\n- However, I couldn't find papers specifically authored solely by her about personal information\\n\\nWould you like me to elaborate on any of these findings?\", type='text')], model='claude-3-5-haiku-20241022', role='assistant', stop_reason='end_turn', stop_sequence=None, type='message', usage=Usage(cache_creation_input_tokens=0, cache_read_input_tokens=0, input_tokens=9257, output_tokens=165, server_tool_use=None, service_tier='standard'))\n",
      "FINAL RESPONSE:\n",
      "Interestingly, I found a connection to personal information in the BigCode project paper, which mentions work on Personally Identifiable Information (PII) redaction. However, while Loubna Ben Allal is associated with this project, none of the papers I found are exclusively written by her about personal information.\n",
      "\n",
      "To summarize:\n",
      "- Loubna Ben Allal is a Research Engineer at Hugging Face\n",
      "- She has been involved in projects related to language models\n",
      "- She's part of the BigCode project, which includes work on PII (Personally Identifiable Information) redaction\n",
      "- However, I couldn't find papers specifically authored solely by her about personal information\n",
      "\n",
      "Would you like me to elaborate on any of these findings?\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "# multi-turn conversation - sometimes this will use both tools, too\n",
    "messages = run_agent(\"Are any of these written by Loubna Ben Allal?\", messages=messages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multi-step tool use\n",
    "\n",
    "In Multi-step tool use, the LLM engages in \"planning\" to determine what tools need to be called and the sequence in which they will be called, to answer the user query. This enables the capability for the LLM to autonomously perform complex tasks using a Thought-Action-Observation reasoning series.\n",
    "\n",
    "Compared to the single-step tool use, in multi-step tool use the output of the initial tool is an input to the subsequent tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QUESTION:\n",
      "What papers has Loubna Ben Allal authored about personal information\n",
      "==================================================\n",
      "INITIAL RESPONSE:\n",
      "Message(id='msg_015M3SogetiX3q8FfnWDKuLj', content=[TextBlock(citations=None, text=\"Let me search for information about Loubna Ben Allal's research papers related to personal information.\", type='text'), ToolUseBlock(id='toolu_012JvzM4CSnwhZzqfg7FzDUp', input={'query': 'Loubna Ben Allal papers on personal information privacy research'}, name='web_search_engine', type='tool_use')], model='claude-3-5-haiku-20241022', role='assistant', stop_reason='tool_use', stop_sequence=None, type='message', usage=Usage(cache_creation_input_tokens=0, cache_read_input_tokens=0, input_tokens=442, output_tokens=91, server_tool_use=None, service_tier='standard'))\n",
      "TOOL CALLS:\n",
      "[TextBlock(citations=None, text=\"Let me search for information about Loubna Ben Allal's research papers related to personal information.\", type='text'), ToolUseBlock(id='toolu_012JvzM4CSnwhZzqfg7FzDUp', input={'query': 'Loubna Ben Allal papers on personal information privacy research'}, name='web_search_engine', type='tool_use')]\n",
      "Not a tool_use content block: TextBlock(citations=None, text=\"Let me search for information about Loubna Ben Allal's research papers related to personal information.\", type='text')\n",
      "Tool name: web_search_engine | Parameters: {'query': 'Loubna Ben Allal papers on personal information privacy research'}\n",
      "==================================================\n",
      "calling step 4 with:\n",
      "[{'role': 'user', 'content': 'What papers has Loubna Ben Allal authored about personal information'}, {'role': 'assistant', 'content': [TextBlock(citations=None, text=\"Let me search for information about Loubna Ben Allal's research papers related to personal information.\", type='text'), ToolUseBlock(id='toolu_012JvzM4CSnwhZzqfg7FzDUp', input={'query': 'Loubna Ben Allal papers on personal information privacy research'}, name='web_search_engine', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_012JvzM4CSnwhZzqfg7FzDUp', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'Loubna Ben Allal - Home - ACM Digital Library\\', \\'href\\': \\'https://dl.acm.org/profile/99661625996\\', \\'body\\': \"Colleague Collaboration Author\\'s Latest Publications research-article Scaling laws and compute-optimal training beyond fixed training durations Alexander Hägele, Elie Bakouch, Atli Kosson, + 3\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'LOUBNA BEN ALLAL - Google Scholar\\', \\'href\\': \\'https://scholar.google.com/citations?user=reU1i-sAAAAJ\\', \\'body\\': \\'\\\\u202aHugging Face\\\\u202c - \\\\u202a\\\\u202aCited by 4,518\\\\u202c\\\\u202c - \\\\u202aMachine Learning\\\\u202c\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'arXiv:2301.03988v2 [cs.SE] 24 Feb 2023\\', \\'href\\': \\'https://arxiv.org/pdf/2301.03988\\', \\'body\\': \\'The BigCode project is an open-scientific collaboration working on the responsi-ble development of large language models for code.1 This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experi-ments conducted to de-risk the model architecture, and the experiments ...\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'(PDF) StarCoder: may the source be with you! - ResearchGate\\', \\'href\\': \\'https://www.researchgate.net/publication/370656499_StarCoder_may_the_source_be_with_you\\', \\'body\\': \"Concerns about personal information led Italy to temporarily ban ChatGPT and launch an on- going investigation into OpenAI\\' s compliance with the E.U. \\'s General Data Protection Regula-\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'GitHub Pages - Loubna Ben Allal\\', \\'href\\': \\'https://loubnabnl.github.io/\\', \\'body\\': \"Welcome to my personal page! 🌸 I\\'m Loubna, a Research Engineer at Hugging Face. I lead efforts on training small Language Models (SmolLM & SmolLM2) and building pre-training datasets like Cosmopedia and FineWeb-Edu.\"}}]'}]}]\n",
      "RESPONSE:\n",
      "Message(id='msg_012tezYcxbFdJ7akkN77cgzG', content=[TextBlock(citations=None, text='Let me do a more specific search in the Pinecone vector database:', type='text'), ToolUseBlock(id='toolu_01PJgrmu7PrPuiDLcKnk5vJV', input={'query': 'Loubna Ben Allal personal information privacy research'}, name='pinecone_vector_search', type='tool_use')], model='claude-3-5-haiku-20241022', role='assistant', stop_reason='tool_use', stop_sequence=None, type='message', usage=Usage(cache_creation_input_tokens=0, cache_read_input_tokens=0, input_tokens=1158, output_tokens=86, server_tool_use=None, service_tier='standard'))\n",
      "TOOL CALLS:\n",
      "[TextBlock(citations=None, text='Let me do a more specific search in the Pinecone vector database:', type='text'), ToolUseBlock(id='toolu_01PJgrmu7PrPuiDLcKnk5vJV', input={'query': 'Loubna Ben Allal personal information privacy research'}, name='pinecone_vector_search', type='tool_use')]\n",
      "Not a tool_use content block: TextBlock(citations=None, text='Let me do a more specific search in the Pinecone vector database:', type='text')\n",
      "Tool name: pinecone_vector_search | Parameters: {'query': 'Loubna Ben Allal personal information privacy research'}\n",
      "==================================================\n",
      "calling step 4 with:\n",
      "[{'role': 'user', 'content': 'What papers has Loubna Ben Allal authored about personal information'}, {'role': 'assistant', 'content': [TextBlock(citations=None, text=\"Let me search for information about Loubna Ben Allal's research papers related to personal information.\", type='text'), ToolUseBlock(id='toolu_012JvzM4CSnwhZzqfg7FzDUp', input={'query': 'Loubna Ben Allal papers on personal information privacy research'}, name='web_search_engine', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_012JvzM4CSnwhZzqfg7FzDUp', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'Loubna Ben Allal - Home - ACM Digital Library\\', \\'href\\': \\'https://dl.acm.org/profile/99661625996\\', \\'body\\': \"Colleague Collaboration Author\\'s Latest Publications research-article Scaling laws and compute-optimal training beyond fixed training durations Alexander Hägele, Elie Bakouch, Atli Kosson, + 3\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'LOUBNA BEN ALLAL - Google Scholar\\', \\'href\\': \\'https://scholar.google.com/citations?user=reU1i-sAAAAJ\\', \\'body\\': \\'\\\\u202aHugging Face\\\\u202c - \\\\u202a\\\\u202aCited by 4,518\\\\u202c\\\\u202c - \\\\u202aMachine Learning\\\\u202c\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'arXiv:2301.03988v2 [cs.SE] 24 Feb 2023\\', \\'href\\': \\'https://arxiv.org/pdf/2301.03988\\', \\'body\\': \\'The BigCode project is an open-scientific collaboration working on the responsi-ble development of large language models for code.1 This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experi-ments conducted to de-risk the model architecture, and the experiments ...\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'(PDF) StarCoder: may the source be with you! - ResearchGate\\', \\'href\\': \\'https://www.researchgate.net/publication/370656499_StarCoder_may_the_source_be_with_you\\', \\'body\\': \"Concerns about personal information led Italy to temporarily ban ChatGPT and launch an on- going investigation into OpenAI\\' s compliance with the E.U. \\'s General Data Protection Regula-\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'GitHub Pages - Loubna Ben Allal\\', \\'href\\': \\'https://loubnabnl.github.io/\\', \\'body\\': \"Welcome to my personal page! 🌸 I\\'m Loubna, a Research Engineer at Hugging Face. I lead efforts on training small Language Models (SmolLM & SmolLM2) and building pre-training datasets like Cosmopedia and FineWeb-Edu.\"}}]'}]}, {'role': 'assistant', 'content': [TextBlock(citations=None, text='Let me do a more specific search in the Pinecone vector database:', type='text'), ToolUseBlock(id='toolu_01PJgrmu7PrPuiDLcKnk5vJV', input={'query': 'Loubna Ben Allal personal information privacy research'}, name='pinecone_vector_search', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01PJgrmu7PrPuiDLcKnk5vJV', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2202.00443\\',\\n \\'_score\\': 0.0022694687359035015,\\n \\'fields\\': {\\'authors\\': \\'Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi \\'\\n                       \\'Papadopoulou, David Sánchez, Montserrat Batet\\',\\n            \\'categories\\': \\'cs.CL, cs.AI\\',\\n            \\'chunk_text\\': \\'We present a novel benchmark and associated \\'\\n                          \\'evaluation metrics for assessing\\\\n\\'\\n                          \\'the performance of text anonymization methods. Text \\'\\n                          \\'anonymization, defined as\\\\n\\'\\n                          \\'the task of editing a text document to prevent the \\'\\n                          \\'disclosure of personal\\\\n\\'\\n                          \\'information, currently suffers from a shortage of \\'\\n                          \\'privacy-oriented annotated\\\\n\\'\\n                          \\'text resources, making it difficult to properly \\'\\n                          \\'evaluate the level of privacy\\\\n\\'\\n                          \\'protection offered by various anonymization \\'\\n                          \\'methods. This paper presents TAB\\\\n\\'\\n                          \\'(Text Anonymization Benchmark), a new, open-source \\'\\n                          \\'annotated corpus developed\\\\n\\'\\n                          \\'to address this shortage. The corpus comprises \\'\\n                          \\'1,268 English-language court\\\\n\\'\\n                          \\'cases from the European Court of Human Rights \\'\\n                          \\'(ECHR) enriched with\\\\n\\'\\n                          \\'comprehensive annotations about the personal \\'\\n                          \\'information appearing in each\\\\n\\'\\n                          \\'document, including their semantic category, \\'\\n                          \\'identifier type, confidential\\\\n\\'\\n                          \\'attributes, and co-reference relations. Compared to \\'\\n                          \\'previous work, the TAB\\\\n\\'\\n                          \\'corpus is designed to go beyond traditional \\'\\n                          \\'de-identification (which is limited\\\\n\\'\\n                          \\'to the detection of predefined semantic \\'\\n                          \\'categories), and explicitly marks which\\\\n\\'\\n                          \\'text spans ought to be masked in order to conceal \\'\\n                          \\'the identity of the person to\\\\n\\'\\n                          \\'be protected. Along with presenting the corpus and \\'\\n                          \\'its annotation layers, we\\\\n\\'\\n                          \\'also propose a set of evaluation metrics that are \\'\\n                          \\'specifically tailored towards\\\\n\\'\\n                          \\'measuring the performance of text anonymization, \\'\\n                          \\'both in terms of privacy\\\\n\\'\\n                          \\'protection and utility preservation. We illustrate \\'\\n                          \\'the use of the benchmark and\\\\n\\'\\n                          \\'the proposed metrics by assessing the empirical \\'\\n                          \\'performance of several baseline\\\\n\\'\\n                          \\'text anonymization models. The full corpus along \\'\\n                          \\'with its privacy-oriented\\\\n\\'\\n                          \\'annotation guidelines, evaluation scripts and \\'\\n                          \\'baseline models are available on:\\\\n\\'\\n                          \\'https://github.com/NorskRegnesentral/text-anonymisation-benchmark\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20220125\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2202.00443\\',\\n            \\'title\\': \\'The Text Anonymization Benchmark (TAB): A Dedicated \\'\\n                     \\'Corpus and Evaluation Framework for Text Anonymization\\',\\n            \\'updated\\': \\'20220701\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2209.02299\\',\\n \\'_score\\': 0.001674399245530367,\\n \\'fields\\': {\\'authors\\': \\'Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, \\'\\n                       \\'Alan Wee-Chung Liew, Hongzhi Yin, Quoc Viet Hung \\'\\n                       \\'Nguyen\\',\\n            \\'categories\\': \\'cs.LG, cs.AI\\',\\n            \\'chunk_text\\': \\'Today, computer systems hold large amounts of \\'\\n                          \\'personal data. Yet while such\\\\n\\'\\n                          \\'an abundance of data allows breakthroughs in \\'\\n                          \\'artificial intelligence, and\\\\n\\'\\n                          \\'especially machine learning (ML), its existence can \\'\\n                          \\'be a threat to user\\\\n\\'\\n                          \\'privacy, and it can weaken the bonds of trust \\'\\n                          \\'between humans and AI. Recent\\\\n\\'\\n                          \\'regulations now require that, on request, private \\'\\n                          \\'information about a user must\\\\n\\'\\n                          \\'be removed from both computer systems and from ML \\'\\n                          \\'models, i.e. ``the right to\\\\n\\'\\n                          \"be forgotten\\'\\'). While removing data from back-end \"\\n                          \\'databases should be\\\\n\\'\\n                          \\'straightforward, it is not sufficient in the AI \\'\\n                          \\'context as ML models often\\\\n\\'\\n                          \"`remember\\' the old data. Contemporary adversarial \"\\n                          \\'attacks on trained models\\\\n\\'\\n                          \\'have proven that we can learn whether an instance \\'\\n                          \\'or an attribute belonged to\\\\n\\'\\n                          \\'the training data. This phenomenon calls for a new \\'\\n                          \\'paradigm, namely machine\\\\n\\'\\n                          \\'unlearning, to make ML models forget about \\'\\n                          \\'particular data. It turns out that\\\\n\\'\\n                          \\'recent works on machine unlearning have not been \\'\\n                          \\'able to completely solve the\\\\n\\'\\n                          \\'problem due to the lack of common frameworks and \\'\\n                          \\'resources. Therefore, this\\\\n\\'\\n                          \\'paper aspires to present a comprehensive \\'\\n                          \"examination of machine unlearning\\'s\\\\n\"\\n                          \\'concepts, scenarios, methods, and applications. \\'\\n                          \\'Specifically, as a category\\\\n\\'\\n                          \\'collection of cutting-edge studies, the intention \\'\\n                          \\'behind this article is to\\\\n\\'\\n                          \\'serve as a comprehensive resource for researchers \\'\\n                          \\'and practitioners seeking an\\\\n\\'\\n                          \\'introduction to machine unlearning and its \\'\\n                          \\'formulations, design criteria,\\\\n\\'\\n                          \\'removal requests, algorithms, and applications. In \\'\\n                          \\'addition, we aim to\\\\n\\'\\n                          \\'highlight the key findings, current trends, and new \\'\\n                          \\'research areas that have\\\\n\\'\\n                          \\'not yet featured the use of machine unlearning but \\'\\n                          \\'could benefit greatly from\\\\n\\'\\n                          \\'it. We hope this survey serves as a valuable \\'\\n                          \\'resource for ML researchers and\\\\n\\'\\n                          \\'those seeking to innovate privacy technologies. Our \\'\\n                          \\'resources are publicly\\\\n\\'\\n                          \\'available at \\'\\n                          \\'https://github.com/tamlhp/awesome-machine-unlearning.\\',\\n            \\'primary_category\\': \\'cs.LG\\',\\n            \\'published\\': \\'20220906\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2209.02299\\',\\n            \\'title\\': \\'A Survey of Machine Unlearning\\',\\n            \\'updated\\': \\'20221021\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2202.05520\\',\\n \\'_score\\': 0.0010733063099905849,\\n \\'fields\\': {\\'authors\\': \\'Hannah Brown, Katherine Lee, Fatemehsadat \\'\\n                       \\'Mireshghallah, Reza Shokri, Florian Tramèr\\',\\n            \\'categories\\': \\'stat.ML, cs.CL, cs.LG\\',\\n            \\'chunk_text\\': \\'Natural language reflects our private lives and \\'\\n                          \\'identities, making its\\\\n\\'\\n                          \\'privacy concerns as broad as those of real life. \\'\\n                          \\'Language models lack the\\\\n\\'\\n                          \\'ability to understand the context and sensitivity \\'\\n                          \\'of text, and tend to memorize\\\\n\\'\\n                          \\'phrases present in their training sets. An \\'\\n                          \\'adversary can exploit this tendency\\\\n\\'\\n                          \\'to extract training data. Depending on the nature \\'\\n                          \\'of the content and the\\\\n\\'\\n                          \\'context in which this data was collected, this \\'\\n                          \\'could violate expectations of\\\\n\\'\\n                          \\'privacy. Thus there is a growing interest in \\'\\n                          \\'techniques for training language\\\\n\\'\\n                          \\'models that preserve privacy. In this paper, we \\'\\n                          \\'discuss the mismatch between\\\\n\\'\\n                          \\'the narrow assumptions made by popular data \\'\\n                          \\'protection techniques (data\\\\n\\'\\n                          \\'sanitization and differential privacy), and the \\'\\n                          \\'broadness of natural language\\\\n\\'\\n                          \\'and of privacy as a social norm. We argue that \\'\\n                          \\'existing protection methods\\\\n\\'\\n                          \\'cannot guarantee a generic and meaningful notion of \\'\\n                          \\'privacy for language\\\\n\\'\\n                          \\'models. We conclude that language models should be \\'\\n                          \\'trained on text data which\\\\n\\'\\n                          \\'was explicitly produced for public use.\\',\\n            \\'primary_category\\': \\'stat.ML\\',\\n            \\'published\\': \\'20220211\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2202.05520\\',\\n            \\'title\\': \\'What Does it Mean for a Language Model to Preserve \\'\\n                     \\'Privacy?\\',\\n            \\'updated\\': \\'20220214\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2305.02547\\',\\n \\'_score\\': 0.0008559006964787841,\\n \\'fields\\': {\\'authors\\': \\'Hang Jiang, Xiajie Zhang, Xubo Cao, Jad Kabbara\\',\\n            \\'categories\\': \\'cs.CL, cs.AI, cs.HC\\',\\n            \\'chunk_text\\': \\'Despite the many use cases for large language \\'\\n                          \\'models (LLMs) in creating\\\\n\\'\\n                          \\'personalized chatbots, there has been limited \\'\\n                          \\'research on evaluating the extent\\\\n\\'\\n                          \\'to which the behaviors of personalized LLMs \\'\\n                          \\'accurately and consistently reflect\\\\n\\'\\n                          \\'specific personality traits. We consider studying \\'\\n                          \\'the behavior of LLM-based\\\\n\\'\\n                          \\'agents, referred to as LLM personas, and present a \\'\\n                          \\'case study with ChatGPT and\\\\n\\'\\n                          \\'GPT-4. The study investigates whether LLMs can \\'\\n                          \\'generate content that aligns\\\\n\\'\\n                          \\'with their assigned personality profiles. To this \\'\\n                          \\'end, we create distinct LLM\\\\n\\'\\n                          \\'personas based on the Big Five personality model, \\'\\n                          \\'have them complete the\\\\n\\'\\n                          \\'44-item Big Five Inventory (BFI) personality test \\'\\n                          \\'and a story writing task, and\\\\n\\'\\n                          \\'then assess their essays with automatic and human \\'\\n                          \\'evaluations. Results show\\\\n\\'\\n                          \"that LLM personas\\' self-reported BFI scores are \"\\n                          \\'consistent with their\\\\n\\'\\n                          \\'designated personality types, with large effect \\'\\n                          \\'sizes observed across five\\\\n\\'\\n                          \\'traits. Additionally, there are significant \\'\\n                          \\'correlations between the assigned\\\\n\\'\\n                          \\'personality types and certain psycholinguistic \\'\\n                          \\'features of their writings, as\\\\n\\'\\n                          \\'measured by the Linguistic Inquiry and Word Count \\'\\n                          \\'(LIWC) tool. Interestingly,\\\\n\\'\\n                          \\'human evaluators perceive the stories as less \\'\\n                          \\'personal when told that the\\\\n\\'\\n                          \\'stories are authored by AI. However, their \\'\\n                          \\'judgments on other aspects of the\\\\n\\'\\n                          \\'writing such as readability, cohesiveness, \\'\\n                          \\'redundancy, likeability, and\\\\n\\'\\n                          \\'believability remain largely unaffected. Notably, \\'\\n                          \\'when evaluators were informed\\\\n\\'\\n                          \\'about the AI authorship, their accuracy in \\'\\n                          \\'identifying the intended personality\\\\n\\'\\n                          \\'traits from the stories decreased by more than 10% \\'\\n                          \\'for some traits. This\\\\n\\'\\n                          \\'research marks a significant step forward in \\'\\n                          \\'understanding the capabilities of\\\\n\\'\\n                          \\'LLMs to express personality traits.\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20230504\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2305.02547\\',\\n            \\'title\\': \\'PersonaLLM: Investigating the Ability of Large Language \\'\\n                     \\'Models to Express Big Five Personality Traits\\',\\n            \\'updated\\': \\'20231116\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2106.13973\\',\\n \\'_score\\': 0.000539980421308428,\\n \\'fields\\': {\\'authors\\': \\'Priyam Basu, Tiasa Singha Roy, Rakshit Naidu, Zumrut \\'\\n                       \\'Muftuoglu, Sahib Singh, Fatemehsadat Mireshghallah\\',\\n            \\'categories\\': \\'cs.CL, cs.CR, cs.LG\\',\\n            \\'chunk_text\\': \\'Natural Language Processing (NLP) techniques can be \\'\\n                          \\'applied to help with the\\\\n\\'\\n                          \\'diagnosis of medical conditions such as depression, \\'\\n                          \\'using a collection of a\\\\n\\'\\n                          \"person\\'s utterances. Depression is a serious \"\\n                          \\'medical illness that can have\\\\n\\'\\n                          \\'adverse effects on how one feels, thinks, and acts, \\'\\n                          \\'which can lead to emotional\\\\n\\'\\n                          \\'and physical problems. Due to the sensitive nature \\'\\n                          \\'of such data, privacy\\\\n\\'\\n                          \\'measures need to be taken for handling and training \\'\\n                          \\'models with such data. In\\\\n\\'\\n                          \\'this work, we study the effects that the \\'\\n                          \\'application of Differential Privacy\\\\n\\'\\n                          \\'(DP) has, in both a centralized and a Federated \\'\\n                          \\'Learning (FL) setup, on\\\\n\\'\\n                          \\'training contextualized language models (BERT, \\'\\n                          \\'ALBERT, RoBERTa and DistilBERT).\\\\n\\'\\n                          \\'We offer insights on how to privately train NLP \\'\\n                          \\'models and what architectures\\\\n\\'\\n                          \\'and setups provide more desirable privacy utility \\'\\n                          \\'trade-offs. We envisage this\\\\n\\'\\n                          \\'work to be used in future healthcare and mental \\'\\n                          \\'health studies to keep medical\\\\n\\'\\n                          \\'history private. Therefore, we provide an \\'\\n                          \\'open-source implementation of this\\\\n\\'\\n                          \\'work.\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20210626\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2106.13973\\',\\n            \\'title\\': \\'Benchmarking Differential Privacy and Federated Learning \\'\\n                     \\'for BERT Models\\',\\n            \\'updated\\': \\'20220616\\'}}}]'}]}]\n",
      "RESPONSE:\n",
      "Message(id='msg_01N4RaULzDwMNdfvB64SqbfT', content=[TextBlock(citations=None, text=\"Based on the search results, I found some interesting information about Loubna Ben Allal's work related to personal information:\\n\\n1. BigCode Project Paper: She was involved in a research paper about the BigCode project, which specifically addressed Personally Identifiable Information (PII) redaction pipeline. The paper discusses efforts to responsibly develop large language models for code while managing personal information risks.\\n\\n2. Research at Hugging Face: According to her personal page, she is a Research Engineer at Hugging Face, where she leads efforts on training small Language Models and building pre-training datasets like Cosmopedia and FineWeb-Edu.\\n\\nHowever, to get a more comprehensive view of her specific work on personal information, I'll do one more search:\", type='text'), ToolUseBlock(id='toolu_01WeEt54vy1vi6ztjTuze5KG', input={'query': 'Loubna Ben Allal research on personal information privacy in AI'}, name='web_search_engine', type='tool_use')], model='claude-3-5-haiku-20241022', role='assistant', stop_reason='tool_use', stop_sequence=None, type='message', usage=Usage(cache_creation_input_tokens=0, cache_read_input_tokens=0, input_tokens=4891, output_tokens=234, server_tool_use=None, service_tier='standard'))\n",
      "TOOL CALLS:\n",
      "[TextBlock(citations=None, text=\"Based on the search results, I found some interesting information about Loubna Ben Allal's work related to personal information:\\n\\n1. BigCode Project Paper: She was involved in a research paper about the BigCode project, which specifically addressed Personally Identifiable Information (PII) redaction pipeline. The paper discusses efforts to responsibly develop large language models for code while managing personal information risks.\\n\\n2. Research at Hugging Face: According to her personal page, she is a Research Engineer at Hugging Face, where she leads efforts on training small Language Models and building pre-training datasets like Cosmopedia and FineWeb-Edu.\\n\\nHowever, to get a more comprehensive view of her specific work on personal information, I'll do one more search:\", type='text'), ToolUseBlock(id='toolu_01WeEt54vy1vi6ztjTuze5KG', input={'query': 'Loubna Ben Allal research on personal information privacy in AI'}, name='web_search_engine', type='tool_use')]\n",
      "Not a tool_use content block: TextBlock(citations=None, text=\"Based on the search results, I found some interesting information about Loubna Ben Allal's work related to personal information:\\n\\n1. BigCode Project Paper: She was involved in a research paper about the BigCode project, which specifically addressed Personally Identifiable Information (PII) redaction pipeline. The paper discusses efforts to responsibly develop large language models for code while managing personal information risks.\\n\\n2. Research at Hugging Face: According to her personal page, she is a Research Engineer at Hugging Face, where she leads efforts on training small Language Models and building pre-training datasets like Cosmopedia and FineWeb-Edu.\\n\\nHowever, to get a more comprehensive view of her specific work on personal information, I'll do one more search:\", type='text')\n",
      "Tool name: web_search_engine | Parameters: {'query': 'Loubna Ben Allal research on personal information privacy in AI'}\n",
      "==================================================\n",
      "calling step 4 with:\n",
      "[{'role': 'user', 'content': 'What papers has Loubna Ben Allal authored about personal information'}, {'role': 'assistant', 'content': [TextBlock(citations=None, text=\"Let me search for information about Loubna Ben Allal's research papers related to personal information.\", type='text'), ToolUseBlock(id='toolu_012JvzM4CSnwhZzqfg7FzDUp', input={'query': 'Loubna Ben Allal papers on personal information privacy research'}, name='web_search_engine', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_012JvzM4CSnwhZzqfg7FzDUp', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'Loubna Ben Allal - Home - ACM Digital Library\\', \\'href\\': \\'https://dl.acm.org/profile/99661625996\\', \\'body\\': \"Colleague Collaboration Author\\'s Latest Publications research-article Scaling laws and compute-optimal training beyond fixed training durations Alexander Hägele, Elie Bakouch, Atli Kosson, + 3\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'LOUBNA BEN ALLAL - Google Scholar\\', \\'href\\': \\'https://scholar.google.com/citations?user=reU1i-sAAAAJ\\', \\'body\\': \\'\\\\u202aHugging Face\\\\u202c - \\\\u202a\\\\u202aCited by 4,518\\\\u202c\\\\u202c - \\\\u202aMachine Learning\\\\u202c\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'arXiv:2301.03988v2 [cs.SE] 24 Feb 2023\\', \\'href\\': \\'https://arxiv.org/pdf/2301.03988\\', \\'body\\': \\'The BigCode project is an open-scientific collaboration working on the responsi-ble development of large language models for code.1 This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experi-ments conducted to de-risk the model architecture, and the experiments ...\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'(PDF) StarCoder: may the source be with you! - ResearchGate\\', \\'href\\': \\'https://www.researchgate.net/publication/370656499_StarCoder_may_the_source_be_with_you\\', \\'body\\': \"Concerns about personal information led Italy to temporarily ban ChatGPT and launch an on- going investigation into OpenAI\\' s compliance with the E.U. \\'s General Data Protection Regula-\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'GitHub Pages - Loubna Ben Allal\\', \\'href\\': \\'https://loubnabnl.github.io/\\', \\'body\\': \"Welcome to my personal page! 🌸 I\\'m Loubna, a Research Engineer at Hugging Face. I lead efforts on training small Language Models (SmolLM & SmolLM2) and building pre-training datasets like Cosmopedia and FineWeb-Edu.\"}}]'}]}, {'role': 'assistant', 'content': [TextBlock(citations=None, text='Let me do a more specific search in the Pinecone vector database:', type='text'), ToolUseBlock(id='toolu_01PJgrmu7PrPuiDLcKnk5vJV', input={'query': 'Loubna Ben Allal personal information privacy research'}, name='pinecone_vector_search', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01PJgrmu7PrPuiDLcKnk5vJV', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2202.00443\\',\\n \\'_score\\': 0.0022694687359035015,\\n \\'fields\\': {\\'authors\\': \\'Ildikó Pilán, Pierre Lison, Lilja Øvrelid, Anthi \\'\\n                       \\'Papadopoulou, David Sánchez, Montserrat Batet\\',\\n            \\'categories\\': \\'cs.CL, cs.AI\\',\\n            \\'chunk_text\\': \\'We present a novel benchmark and associated \\'\\n                          \\'evaluation metrics for assessing\\\\n\\'\\n                          \\'the performance of text anonymization methods. Text \\'\\n                          \\'anonymization, defined as\\\\n\\'\\n                          \\'the task of editing a text document to prevent the \\'\\n                          \\'disclosure of personal\\\\n\\'\\n                          \\'information, currently suffers from a shortage of \\'\\n                          \\'privacy-oriented annotated\\\\n\\'\\n                          \\'text resources, making it difficult to properly \\'\\n                          \\'evaluate the level of privacy\\\\n\\'\\n                          \\'protection offered by various anonymization \\'\\n                          \\'methods. This paper presents TAB\\\\n\\'\\n                          \\'(Text Anonymization Benchmark), a new, open-source \\'\\n                          \\'annotated corpus developed\\\\n\\'\\n                          \\'to address this shortage. The corpus comprises \\'\\n                          \\'1,268 English-language court\\\\n\\'\\n                          \\'cases from the European Court of Human Rights \\'\\n                          \\'(ECHR) enriched with\\\\n\\'\\n                          \\'comprehensive annotations about the personal \\'\\n                          \\'information appearing in each\\\\n\\'\\n                          \\'document, including their semantic category, \\'\\n                          \\'identifier type, confidential\\\\n\\'\\n                          \\'attributes, and co-reference relations. Compared to \\'\\n                          \\'previous work, the TAB\\\\n\\'\\n                          \\'corpus is designed to go beyond traditional \\'\\n                          \\'de-identification (which is limited\\\\n\\'\\n                          \\'to the detection of predefined semantic \\'\\n                          \\'categories), and explicitly marks which\\\\n\\'\\n                          \\'text spans ought to be masked in order to conceal \\'\\n                          \\'the identity of the person to\\\\n\\'\\n                          \\'be protected. Along with presenting the corpus and \\'\\n                          \\'its annotation layers, we\\\\n\\'\\n                          \\'also propose a set of evaluation metrics that are \\'\\n                          \\'specifically tailored towards\\\\n\\'\\n                          \\'measuring the performance of text anonymization, \\'\\n                          \\'both in terms of privacy\\\\n\\'\\n                          \\'protection and utility preservation. We illustrate \\'\\n                          \\'the use of the benchmark and\\\\n\\'\\n                          \\'the proposed metrics by assessing the empirical \\'\\n                          \\'performance of several baseline\\\\n\\'\\n                          \\'text anonymization models. The full corpus along \\'\\n                          \\'with its privacy-oriented\\\\n\\'\\n                          \\'annotation guidelines, evaluation scripts and \\'\\n                          \\'baseline models are available on:\\\\n\\'\\n                          \\'https://github.com/NorskRegnesentral/text-anonymisation-benchmark\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20220125\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2202.00443\\',\\n            \\'title\\': \\'The Text Anonymization Benchmark (TAB): A Dedicated \\'\\n                     \\'Corpus and Evaluation Framework for Text Anonymization\\',\\n            \\'updated\\': \\'20220701\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2209.02299\\',\\n \\'_score\\': 0.001674399245530367,\\n \\'fields\\': {\\'authors\\': \\'Thanh Tam Nguyen, Thanh Trung Huynh, Phi Le Nguyen, \\'\\n                       \\'Alan Wee-Chung Liew, Hongzhi Yin, Quoc Viet Hung \\'\\n                       \\'Nguyen\\',\\n            \\'categories\\': \\'cs.LG, cs.AI\\',\\n            \\'chunk_text\\': \\'Today, computer systems hold large amounts of \\'\\n                          \\'personal data. Yet while such\\\\n\\'\\n                          \\'an abundance of data allows breakthroughs in \\'\\n                          \\'artificial intelligence, and\\\\n\\'\\n                          \\'especially machine learning (ML), its existence can \\'\\n                          \\'be a threat to user\\\\n\\'\\n                          \\'privacy, and it can weaken the bonds of trust \\'\\n                          \\'between humans and AI. Recent\\\\n\\'\\n                          \\'regulations now require that, on request, private \\'\\n                          \\'information about a user must\\\\n\\'\\n                          \\'be removed from both computer systems and from ML \\'\\n                          \\'models, i.e. ``the right to\\\\n\\'\\n                          \"be forgotten\\'\\'). While removing data from back-end \"\\n                          \\'databases should be\\\\n\\'\\n                          \\'straightforward, it is not sufficient in the AI \\'\\n                          \\'context as ML models often\\\\n\\'\\n                          \"`remember\\' the old data. Contemporary adversarial \"\\n                          \\'attacks on trained models\\\\n\\'\\n                          \\'have proven that we can learn whether an instance \\'\\n                          \\'or an attribute belonged to\\\\n\\'\\n                          \\'the training data. This phenomenon calls for a new \\'\\n                          \\'paradigm, namely machine\\\\n\\'\\n                          \\'unlearning, to make ML models forget about \\'\\n                          \\'particular data. It turns out that\\\\n\\'\\n                          \\'recent works on machine unlearning have not been \\'\\n                          \\'able to completely solve the\\\\n\\'\\n                          \\'problem due to the lack of common frameworks and \\'\\n                          \\'resources. Therefore, this\\\\n\\'\\n                          \\'paper aspires to present a comprehensive \\'\\n                          \"examination of machine unlearning\\'s\\\\n\"\\n                          \\'concepts, scenarios, methods, and applications. \\'\\n                          \\'Specifically, as a category\\\\n\\'\\n                          \\'collection of cutting-edge studies, the intention \\'\\n                          \\'behind this article is to\\\\n\\'\\n                          \\'serve as a comprehensive resource for researchers \\'\\n                          \\'and practitioners seeking an\\\\n\\'\\n                          \\'introduction to machine unlearning and its \\'\\n                          \\'formulations, design criteria,\\\\n\\'\\n                          \\'removal requests, algorithms, and applications. In \\'\\n                          \\'addition, we aim to\\\\n\\'\\n                          \\'highlight the key findings, current trends, and new \\'\\n                          \\'research areas that have\\\\n\\'\\n                          \\'not yet featured the use of machine unlearning but \\'\\n                          \\'could benefit greatly from\\\\n\\'\\n                          \\'it. We hope this survey serves as a valuable \\'\\n                          \\'resource for ML researchers and\\\\n\\'\\n                          \\'those seeking to innovate privacy technologies. Our \\'\\n                          \\'resources are publicly\\\\n\\'\\n                          \\'available at \\'\\n                          \\'https://github.com/tamlhp/awesome-machine-unlearning.\\',\\n            \\'primary_category\\': \\'cs.LG\\',\\n            \\'published\\': \\'20220906\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2209.02299\\',\\n            \\'title\\': \\'A Survey of Machine Unlearning\\',\\n            \\'updated\\': \\'20221021\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2202.05520\\',\\n \\'_score\\': 0.0010733063099905849,\\n \\'fields\\': {\\'authors\\': \\'Hannah Brown, Katherine Lee, Fatemehsadat \\'\\n                       \\'Mireshghallah, Reza Shokri, Florian Tramèr\\',\\n            \\'categories\\': \\'stat.ML, cs.CL, cs.LG\\',\\n            \\'chunk_text\\': \\'Natural language reflects our private lives and \\'\\n                          \\'identities, making its\\\\n\\'\\n                          \\'privacy concerns as broad as those of real life. \\'\\n                          \\'Language models lack the\\\\n\\'\\n                          \\'ability to understand the context and sensitivity \\'\\n                          \\'of text, and tend to memorize\\\\n\\'\\n                          \\'phrases present in their training sets. An \\'\\n                          \\'adversary can exploit this tendency\\\\n\\'\\n                          \\'to extract training data. Depending on the nature \\'\\n                          \\'of the content and the\\\\n\\'\\n                          \\'context in which this data was collected, this \\'\\n                          \\'could violate expectations of\\\\n\\'\\n                          \\'privacy. Thus there is a growing interest in \\'\\n                          \\'techniques for training language\\\\n\\'\\n                          \\'models that preserve privacy. In this paper, we \\'\\n                          \\'discuss the mismatch between\\\\n\\'\\n                          \\'the narrow assumptions made by popular data \\'\\n                          \\'protection techniques (data\\\\n\\'\\n                          \\'sanitization and differential privacy), and the \\'\\n                          \\'broadness of natural language\\\\n\\'\\n                          \\'and of privacy as a social norm. We argue that \\'\\n                          \\'existing protection methods\\\\n\\'\\n                          \\'cannot guarantee a generic and meaningful notion of \\'\\n                          \\'privacy for language\\\\n\\'\\n                          \\'models. We conclude that language models should be \\'\\n                          \\'trained on text data which\\\\n\\'\\n                          \\'was explicitly produced for public use.\\',\\n            \\'primary_category\\': \\'stat.ML\\',\\n            \\'published\\': \\'20220211\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2202.05520\\',\\n            \\'title\\': \\'What Does it Mean for a Language Model to Preserve \\'\\n                     \\'Privacy?\\',\\n            \\'updated\\': \\'20220214\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2305.02547\\',\\n \\'_score\\': 0.0008559006964787841,\\n \\'fields\\': {\\'authors\\': \\'Hang Jiang, Xiajie Zhang, Xubo Cao, Jad Kabbara\\',\\n            \\'categories\\': \\'cs.CL, cs.AI, cs.HC\\',\\n            \\'chunk_text\\': \\'Despite the many use cases for large language \\'\\n                          \\'models (LLMs) in creating\\\\n\\'\\n                          \\'personalized chatbots, there has been limited \\'\\n                          \\'research on evaluating the extent\\\\n\\'\\n                          \\'to which the behaviors of personalized LLMs \\'\\n                          \\'accurately and consistently reflect\\\\n\\'\\n                          \\'specific personality traits. We consider studying \\'\\n                          \\'the behavior of LLM-based\\\\n\\'\\n                          \\'agents, referred to as LLM personas, and present a \\'\\n                          \\'case study with ChatGPT and\\\\n\\'\\n                          \\'GPT-4. The study investigates whether LLMs can \\'\\n                          \\'generate content that aligns\\\\n\\'\\n                          \\'with their assigned personality profiles. To this \\'\\n                          \\'end, we create distinct LLM\\\\n\\'\\n                          \\'personas based on the Big Five personality model, \\'\\n                          \\'have them complete the\\\\n\\'\\n                          \\'44-item Big Five Inventory (BFI) personality test \\'\\n                          \\'and a story writing task, and\\\\n\\'\\n                          \\'then assess their essays with automatic and human \\'\\n                          \\'evaluations. Results show\\\\n\\'\\n                          \"that LLM personas\\' self-reported BFI scores are \"\\n                          \\'consistent with their\\\\n\\'\\n                          \\'designated personality types, with large effect \\'\\n                          \\'sizes observed across five\\\\n\\'\\n                          \\'traits. Additionally, there are significant \\'\\n                          \\'correlations between the assigned\\\\n\\'\\n                          \\'personality types and certain psycholinguistic \\'\\n                          \\'features of their writings, as\\\\n\\'\\n                          \\'measured by the Linguistic Inquiry and Word Count \\'\\n                          \\'(LIWC) tool. Interestingly,\\\\n\\'\\n                          \\'human evaluators perceive the stories as less \\'\\n                          \\'personal when told that the\\\\n\\'\\n                          \\'stories are authored by AI. However, their \\'\\n                          \\'judgments on other aspects of the\\\\n\\'\\n                          \\'writing such as readability, cohesiveness, \\'\\n                          \\'redundancy, likeability, and\\\\n\\'\\n                          \\'believability remain largely unaffected. Notably, \\'\\n                          \\'when evaluators were informed\\\\n\\'\\n                          \\'about the AI authorship, their accuracy in \\'\\n                          \\'identifying the intended personality\\\\n\\'\\n                          \\'traits from the stories decreased by more than 10% \\'\\n                          \\'for some traits. This\\\\n\\'\\n                          \\'research marks a significant step forward in \\'\\n                          \\'understanding the capabilities of\\\\n\\'\\n                          \\'LLMs to express personality traits.\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20230504\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2305.02547\\',\\n            \\'title\\': \\'PersonaLLM: Investigating the Ability of Large Language \\'\\n                     \\'Models to Express Big Five Personality Traits\\',\\n            \\'updated\\': \\'20231116\\'}}}, {\\'type\\': \\'text\\', \\'text\\': {\\'_id\\': \\'2106.13973\\',\\n \\'_score\\': 0.000539980421308428,\\n \\'fields\\': {\\'authors\\': \\'Priyam Basu, Tiasa Singha Roy, Rakshit Naidu, Zumrut \\'\\n                       \\'Muftuoglu, Sahib Singh, Fatemehsadat Mireshghallah\\',\\n            \\'categories\\': \\'cs.CL, cs.CR, cs.LG\\',\\n            \\'chunk_text\\': \\'Natural Language Processing (NLP) techniques can be \\'\\n                          \\'applied to help with the\\\\n\\'\\n                          \\'diagnosis of medical conditions such as depression, \\'\\n                          \\'using a collection of a\\\\n\\'\\n                          \"person\\'s utterances. Depression is a serious \"\\n                          \\'medical illness that can have\\\\n\\'\\n                          \\'adverse effects on how one feels, thinks, and acts, \\'\\n                          \\'which can lead to emotional\\\\n\\'\\n                          \\'and physical problems. Due to the sensitive nature \\'\\n                          \\'of such data, privacy\\\\n\\'\\n                          \\'measures need to be taken for handling and training \\'\\n                          \\'models with such data. In\\\\n\\'\\n                          \\'this work, we study the effects that the \\'\\n                          \\'application of Differential Privacy\\\\n\\'\\n                          \\'(DP) has, in both a centralized and a Federated \\'\\n                          \\'Learning (FL) setup, on\\\\n\\'\\n                          \\'training contextualized language models (BERT, \\'\\n                          \\'ALBERT, RoBERTa and DistilBERT).\\\\n\\'\\n                          \\'We offer insights on how to privately train NLP \\'\\n                          \\'models and what architectures\\\\n\\'\\n                          \\'and setups provide more desirable privacy utility \\'\\n                          \\'trade-offs. We envisage this\\\\n\\'\\n                          \\'work to be used in future healthcare and mental \\'\\n                          \\'health studies to keep medical\\\\n\\'\\n                          \\'history private. Therefore, we provide an \\'\\n                          \\'open-source implementation of this\\\\n\\'\\n                          \\'work.\\',\\n            \\'primary_category\\': \\'cs.CL\\',\\n            \\'published\\': \\'20210626\\',\\n            \\'source\\': \\'http://arxiv.org/pdf/2106.13973\\',\\n            \\'title\\': \\'Benchmarking Differential Privacy and Federated Learning \\'\\n                     \\'for BERT Models\\',\\n            \\'updated\\': \\'20220616\\'}}}]'}]}, {'role': 'assistant', 'content': [TextBlock(citations=None, text=\"Based on the search results, I found some interesting information about Loubna Ben Allal's work related to personal information:\\n\\n1. BigCode Project Paper: She was involved in a research paper about the BigCode project, which specifically addressed Personally Identifiable Information (PII) redaction pipeline. The paper discusses efforts to responsibly develop large language models for code while managing personal information risks.\\n\\n2. Research at Hugging Face: According to her personal page, she is a Research Engineer at Hugging Face, where she leads efforts on training small Language Models and building pre-training datasets like Cosmopedia and FineWeb-Edu.\\n\\nHowever, to get a more comprehensive view of her specific work on personal information, I'll do one more search:\", type='text'), ToolUseBlock(id='toolu_01WeEt54vy1vi6ztjTuze5KG', input={'query': 'Loubna Ben Allal research on personal information privacy in AI'}, name='web_search_engine', type='tool_use')]}, {'role': 'user', 'content': [{'type': 'tool_result', 'tool_use_id': 'toolu_01WeEt54vy1vi6ztjTuze5KG', 'content': '[{\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'arXiv:2301.03988v2 [cs.SE] 24 Feb 2023\\', \\'href\\': \\'https://arxiv.org/pdf/2301.03988\\', \\'body\\': \\'The BigCode project is an open-scientific collaboration working on the responsi-ble development of large language models for code.1 This tech report describes the progress of the collaboration until December 2022, outlining the current state of the Personally Identifiable Information (PII) redaction pipeline, the experi-ments conducted to de-risk the model architecture, and the experiments ...\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'GitHub Pages - Loubna Ben Allal\\', \\'href\\': \\'https://loubnabnl.github.io/\\', \\'body\\': \"Welcome to my personal page! 🌸 I\\'m Loubna, a Research Engineer at Hugging Face. I lead efforts on training small Language Models (SmolLM & SmolLM2) and building pre-training datasets like Cosmopedia and FineWeb-Edu. Previously, I was a member of the core team behind BigCode, where I worked on The Stack dataset, the largest open dataset of source code, as well as the StarCoder and StarCoder2 ...\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'LOUBNA BEN ALLAL - Google Scholar\\', \\'href\\': \\'https://scholar.google.com/citations?user=reU1i-sAAAAJ\\', \\'body\\': \\'\\\\u202aHugging Face\\\\u202c - \\\\u202a\\\\u202aCited by 4,518\\\\u202c\\\\u202c - \\\\u202aMachine Learning\\\\u202c\\'}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'Privacy in an AI Era: How Do We Protect Our Personal Information?\\', \\'href\\': \\'https://hai.stanford.edu/news/privacy-ai-era-how-do-we-protect-our-personal-information\\', \\'body\\': \"So, this is an area where there is a lot of work to do if we\\'re going to have any sense that our personal information is protected from inclusion in AI systems, including very large systems such as foundation models. You note in your report that the focus on individual privacy rights is too limited and we need to consider collective solutions.\"}}, {\\'type\\': \\'text\\', \\'text\\': {\\'title\\': \\'(PDF) StarCoder: may the source be with you! - ResearchGate\\', \\'href\\': \\'https://www.researchgate.net/publication/370656499_StarCoder_may_the_source_be_with_you\\', \\'body\\': \"Concerns about personal information led Italy to temporarily ban ChatGPT and launch an on- going investigation into OpenAI\\' s compliance with the E.U. \\'s General Data Protection Regula-\"}}]'}]}]\n",
      "RESPONSE:\n",
      "Message(id='msg_01MZfqWrtVYdeRva71DcTQMY', content=[TextBlock(citations=None, text=\"Key findings about Loubna Ben Allal's work on personal information:\\n\\n1. BigCode Project (2023): She was a core team member working on:\\n   - Developing a Personally Identifiable Information (PII) redaction pipeline\\n   - Creating The Stack dataset (largest open dataset of source code)\\n   - Responsible development of large language models for code\\n   - Experiments to de-risk model architecture with respect to personal information\\n\\n2. At Hugging Face, she leads efforts on:\\n   - Training small Language Models (SmolLM & SmolLM2)\\n   - Building pre-training datasets (Cosmopedia and FineWeb-Edu)\\n   - Focusing on responsible AI development\\n\\nWhile she doesn't have extensive standalone papers solely about personal information, her work is deeply integrated with responsible AI development, with a particular focus on managing and protecting personally identifiable information in large language models and datasets. Her contributions are primarily through practical implementations and collaborative projects that address privacy concerns in AI systems.\", type='text')], model='claude-3-5-haiku-20241022', role='assistant', stop_reason='end_turn', stop_sequence=None, type='message', usage=Usage(cache_creation_input_tokens=0, cache_read_input_tokens=0, input_tokens=5828, output_tokens=226, server_tool_use=None, service_tier='standard'))\n",
      "FINAL RESPONSE:\n",
      "Key findings about Loubna Ben Allal's work on personal information:\n",
      "\n",
      "1. BigCode Project (2023): She was a core team member working on:\n",
      "   - Developing a Personally Identifiable Information (PII) redaction pipeline\n",
      "   - Creating The Stack dataset (largest open dataset of source code)\n",
      "   - Responsible development of large language models for code\n",
      "   - Experiments to de-risk model architecture with respect to personal information\n",
      "\n",
      "2. At Hugging Face, she leads efforts on:\n",
      "   - Training small Language Models (SmolLM & SmolLM2)\n",
      "   - Building pre-training datasets (Cosmopedia and FineWeb-Edu)\n",
      "   - Focusing on responsible AI development\n",
      "\n",
      "While she doesn't have extensive standalone papers solely about personal information, her work is deeply integrated with responsible AI development, with a particular focus on managing and protecting personally identifiable information in large language models and datasets. Her contributions are primarily through practical implementations and collaborative projects that address privacy concerns in AI systems.\n",
      "==================================================\n"
     ]
    }
   ],
   "source": [
    "# Multi-step tool use\n",
    "messages = run_agent(\"What papers has Loubna Ben Allal authored about personal information?\", messages=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup Indexes\n",
    "\n",
    "Run these when you're done experimenting, to delete the index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "pc.delete_index(name=index_name)\n"
   ]
  }
 ],
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